governance – Technology Liberation Front https://techliberation.com Keeping politicians' hands off the Net & everything else related to technology Thu, 03 Apr 2025 23:20:10 +0000 en-US hourly 1 6772528 Is AI Really an Unregulated Wild West? https://techliberation.com/2023/06/22/is-ai-really-an-unregulated-wild-west/ https://techliberation.com/2023/06/22/is-ai-really-an-unregulated-wild-west/#comments Thu, 22 Jun 2023 15:04:44 +0000 https://techliberation.com/?p=77142

As I noted in a recent interview with James Pethokoukis for his Faster, Please! newsletter, “[t]he current policy debate over artificial intelligence is haunted by many mythologies and mistaken assumptions. The most problematic of these is the widespread belief that AI is completely ungoverned today.” In a recent R Street Institute report and series of other publications, I have documented just how wrong that particular assumption is.

The first thing I try to remind everyone is that the U.S. federal government is absolutely massive—2.1 million employees, 15 cabinet agencies, 50 independent federal commissions and 434 federal departments. Strangely, when policymakers and pundits deliver remarks on AI policy today, they seem to completely ignore all that regulatory capacity while simultaneously casually tossing out proposals to just add more and more layers of regulation and bureaucracy to it. Well, I say why not see if the existing regulations and bureaucracy are working first, and then we can have a chat about what more is needed to fill gaps.

And a lot  is being done on this front. In a new blog post for R Street, I offer a brief summary of some of the most important recent efforts.

  • In January, the National Institute of Standards and Technology released its “ AI Risk Management Framework ,” which was created through a multi-year, multi-stakeholder process. It is intended to help developers and policymakers better understand how to identify and address various types of potential algorithmic risk.
  • The Food and Drug Administration (FDA) has been using its broad regulatory powers  to review and approve AI and ML-enabled medical devices  for many years already, and the agency possesses  broad recall authority  that can address risks that develop from algorithmic or robotic systems. The FDA is currently refining its approach to AI/ML in  a major proceeding .
  • The National Highway Traffic Safety Administration (NHTSA) has been issuing  constant revisions  to its driverless car policy guidelines since 2016. Like the FDA, the NHTSA also has broad recall authority, which it used in February 2023 to  mandate a recall  of Tesla’s full self-driving autonomous driving system, also requiring an over-the-air software update to over 300,000 vehicles that had the software package.
  • In 2021, the Consumer Product Safety Commission agency issued  a major report  highlighting the many policy tools it already has to address AI risks. Like the FDA and NHTSA, the agency has recall authority that can address risks that develop from consumer-facing algorithmic or robotic systems.
  • In April, Securities and Exchange Commission Chairman Gary Gensler told Congress that his agency is  moving to address  AI and predictive data analytics in finance and investing.
  • The Federal Trade Commission (FTC) has become increasingly active on AI policy issues and has noted in a series of  recent   blog   posts  that the agency is ready to use its broad authority to “unfair and deceptive practices,” involving algorithmic claims or applications.
  • The Equal Employment Opportunity Commission (EEOC) recently  released a memo  as part of its “ongoing effort to help ensure that the use of new technologies complies with federal [equal employment opportunity] law.” It outlines how existing employment antidiscrimination laws and policies cover algorithmic technologies.
  • In May, the Consumer Financial Protection Bureau (CFPB) issued a statement clarifying how existing federal anti-discrimination law already applies to complex algorithmic systems used for lending decisions.  The agency also recently released a report on the use of Chatbots in Consumer Finance, and explained the many ways that the “CFPB is actively monitoring the market” for risks associated with these new services.
  • Along with the EEOC, the FTC and the CFPB, the Civil Rights Division of the Department of Justice released  an April joint statement  saying that the agency heads said that they would be looking to take preemptive steps to address algorithmic discrimination.

“This is real-time algorithmic governance in action,” I argue. Again, additional regulatory steps may be needed later to fill gaps in current law, but policymakers should begin by acknowledging that a lot of algorithmic oversight authority exists across the federal government. Meanwhile, the courts and our common law system are also starting to address novel AI problems as cases develop. For more along these lines, see my recent essay on “The Many Ways Government Already Regulates Artificial Intelligence.”

So, next time someone suggests that AI is developing in an unregulated “Wild West,” remind them of all these existing laws, agencies, and regulatory efforts. And then also ask them a different question no one is really exploring currently: Could it be the case that many agencies are already overregulating some algorithmic and autonomous systems? (I’m looking at you, FAA!) Why is no one worried about that possibility as the global AI race with China and other countries intensifies?

Additional Reading :

]]> https://techliberation.com/2023/06/22/is-ai-really-an-unregulated-wild-west/feed/ 25 77142 My Latest Study on AI Governance https://techliberation.com/2023/04/20/my-latest-study-on-ai-governance/ https://techliberation.com/2023/04/20/my-latest-study-on-ai-governance/#comments Thu, 20 Apr 2023 18:25:29 +0000 https://techliberation.com/?p=77114

The R Street Institute has just released my latest study on AI governance and how to address “alignment” concerns in a bottom-up fashion. The 40-page report is entitled, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence.”

My report asks, is it possible to address AI alignment without starting with the Precautionary Principle as the governance baseline default? I explain how that is indeed possible. While some critics claim that no one is seriously trying to deal with AI alignment today, my report explains how no technology in history has been more heavily scrutinized this early in its life-cycle as AI, machine learning and robotics. The number of ethical frameworks out there already is astonishing. We don’t have too few alignment frameworks; we probably have too many!

We need to get serious about bringing some consistency to these efforts and figure out more concrete ways to a culture of safety by embedding ethics-by-design. But there is an equally compelling interest in ensuring that algorithmic innovations are developed and made widely available to society.

Although some safeguards will be needed to minimize certain AI risks, a more agile and iterative governance approach can address these concerns without creating overbearing, top-down mandates, which would hinder algorithmic innovations – especially at a time when America is looking to stay ahead of China and other nations in the global AI race.

My report explores the many ethical frameworks that professional associations have already formulated as well as the various other “soft law” frameworks that have been devised. I also consider how AI auditing and algorithmic impact assessments can be used to help formalize the twin objectives of “ethics-by-design” and keeping “humans in the loop,” which are the two principles that drive most AI governance frameworks. But it is absolutely essential that audits and impact assessments are done right to ensure it does not become an overbearing, compliance-heavy, and politicized nightmare that would undermine algorithmic entrepreneurialism and computational innovation.

Finally, my report reviews the extensive array of existing government agencies and policies that ALREADY govern artificial intelligence and robotics as well as the wide variety of court-based common law solutions that cover algorithmic innovations. The notion that America has no law or regulation covering artificial intelligence today is massively wrong, as my report explains in detail.

I hope you’ll take the time to check out my new report. This and my previous report on “Getting AI Innovation Culture Right” serve as the foundation of everything we have coming on AI and robotics from the R Street Institute. Next up will be a massive study on global AI “existential risks” and national security issues. Stay tuned. Much more to come!

In the meantime, you can find all my recent work here on my “Running List of My Research on AI, ML & Robotics Policy.”

Additional Reading:

]]> https://techliberation.com/2023/04/20/my-latest-study-on-ai-governance/feed/ 4 77114 On “Pausing” AI https://techliberation.com/2023/04/07/on-pausing-ai/ https://techliberation.com/2023/04/07/on-pausing-ai/#comments Fri, 07 Apr 2023 17:36:05 +0000 https://techliberation.com/?p=77111

Recently, the Future of Life Institute released an open letter that included some computer science luminaries and others  calling for a 6-month “pause” on the deployment and research of “giant” artificial intelligence (AI) technologies. Eliezer Yudkowsky, a prominent AI ethicist, then made news by arguing that the “pause” letter did not go far enough and he proposed that governments consider “airstrikes” against data processing centers, or even be open to the use of nuclear weapons. This is, of course, quite insane. Yet, this is the state of the things today as a AI technopanic seems to growing faster than any of the technopanic that I’ve covered in my 31 years in the field of tech policy—and I have covered a lot of them.

In a new joint essay co-authored with Brent Orrell of the American Enterprise Institute and Chris Messerole of Brookings, we argue that “the ‘pause’ we are most in need of is one on dystopian AI thinking.” The three of us recently served on a blue-ribbon  Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation, an independent effort assembled by the U.S. Chamber of Commerce. In our essay, we note how:

Many of these breakthroughs and applications will already take years to work their way through the traditional lifecycle of development, deployment, and adoption and can likely be managed through legal and regulatory systems that are already in place. Civil rights laws, consumer protection regulations, agency recall authority for defective products, and targeted sectoral regulations already govern algorithmic systems, creating enforcement avenues through our courts and by common law standards allowing for development of new regulatory tools that can be developed as actual, rather than anticipated, problems arise.

“Instead of freezing AI we should leverage the legal, regulatory, and informal tools at hand to manage existing and emerging risks while fashioning new tools to respond to new vulnerabilities,” we conclude. Also on the pause idea, it’s worth checking out this excellent essay from Bloomberg Opinion editors on why “An AI ‘Pause’ Would Be a Disaster for Innovation.”

The problem is not with the “pause” per se. Even if the signatories could somehow enforce a worldwide stop-work order, six months probably wouldn’t do much to halt advances in AI. If a brief and partial moratorium draws attention to the need to think seriously about AI safety, it’s hard to see much harm. Unfortunately, a pause seems likely to evolve into a more generalized opposition to progress.

The editors continue on to rightly note:

This is a formula for outright stagnation. No one can ever be fully confident that a given technology or application will only have positive effects. The history of innovation is one of trial and error, risk and reward. One reason why the US leads the world in digital technology — why it’s home to virtually all the biggest tech platforms — is that it did not preemptively constrain the industry with well-meaning but dubious regulation. It’s no accident that all the leading AI efforts are American too.

That is 100% right, and I appreciate the Bloomberg editors linking to my latest study on AI governance when they made this point. In this new R Street Institute study, I explain why “Getting AI Innovation Culture Right,” is essential to make sure we can enjoy the many benefits that algorithmic systems offer, while also staying competitive in the global race for competitive advantage in this space.

That report is the first in a trilogy of big studies on decentralized, flexible governance of artificial intelligence. We can achieve AI safety without crushing top-down bans or unworkable “pauses,” I argue. My next two papers are, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence” (due out April 20th) and “Existential Risks & Global Governance Issues Surrounding AI & Robotics” (due out late May or early June). I’m also working on a co-authored essay taking a deep dive into the idea of AI impact assessments / auditing (late Spring / early Summer).

Relatedly, on April 7th, DeepLearningAI held an event on “Why at 6-Month AI Pause is a Bad Idea” featuring leading AI scientists Andrew Ng and Yann LeCun discussing the trade-offs associated with the proposal. A crucial point made in the discussion is that a pause, especially a pause in the form of a governmental ban, would be a misguided innovation policy decision. They stressed that there will be policy interventions to address targeted risks from specific algorithmic applications, but that it would be a serious mistake to stop the overall development of the underlying technological capabilities. It’s worth watching.

For more on AI policy, here’s a list of some of my latest reports and essays. Much more to come. AI policy will be the biggest tech policy fight of the our lifetimes.

 

 

]]> https://techliberation.com/2023/04/07/on-pausing-ai/feed/ 2 77111 What Policy Vision for Artificial Intelligence? https://techliberation.com/2023/04/02/what-policy-vision-for-artificial-intelligence/ https://techliberation.com/2023/04/02/what-policy-vision-for-artificial-intelligence/#comments Sun, 02 Apr 2023 21:32:49 +0000 https://techliberation.com/?p=77103

In my latest R Street Institute report, I discuss the importance of “Getting AI Innovation Culture Right.” This is the first of a trilogy of major reports on what sort of policy vision and set of governance principles should guide the development of  artificial intelligence (AI), algorithmic systems, machine learning (ML), robotics, and computational science and engineering more generally. More specifically, these reports seek to answer the question, Can we achieve AI safety without innovation-crushing top-down mandates and massive new regulatory bureaucracies? 

These questions are particular pertinent as we just made it through a week in which we’ve seen a major open letter issued that calls for a 6-month freeze on the deployment of AI technologies, while a prominent AI ethicist argued that governments should go further and consider airstrikes data processing centers even if the exchange of nuclear weapons needed to be considered! On top of that, Italy became the first major nation to ban ChatGPT, the popular AI-enabled chatbot created by U.S.-based OpenAI.

My report begins from a different presumption: AI, ML and algorithmic technologies present society with enormously benefits and, while real risks are there, we can find better ways of addressing them. As I summarize:

The danger exists that policy for algorithmic systems could be formulated in such a way that innovations are treated as guilty until proven innocent—i.e., a precautionary principle approach to policy—resulting in many important AI applications never getting off the drawing board. If regulatory impediments block or slow the creation of life-enriching, and even life-saving, AI innovations, that would leave society less well-off and give rise to different types of societal risks.

I argue that it is essential we not trap AI in an “innovation cage” by establishing the wrong policy default for algorithmic governance but instead work through challenges as they come at us. The right policy default for the internet and for AI continues to be “innovation allowed.” But AI risks do require serious governance steps. Luckily, many tools exist and others are being created. While my next major report (due out April 20th) offers far more detail, this paper sketches out some of those mechanisms. 

The goal of algorithmic policy should be for policymakers and innovators to work together to find flexible, iterative, agile, bottom-up governance solutions over time. We can promote a culture of responsibility among leading AI innovators and balance safety and innovation for complex, rapidly evolving computational and computing technologies like AI. This approach is buttressed by existing laws and regulations, as well as common law and the courts.

The new Biden Admin “AI Bill of Rights” unfortunately represents a fear-based model of technology policymaking that breaks from the superior Clinton framework for the internet & digital technology. Our nation’s policy toward AI, robotics & algorithmic innovation should instead embrace a dynamic future and the enormous possibilities that await us.

Please check out my new paper for more details. Much more to come. And you can also check out my running list of research on AI, ML robotics policy.

]]> https://techliberation.com/2023/04/02/what-policy-vision-for-artificial-intelligence/feed/ 3 77103 Studies Document Growing Cost of EU Privacy Regulations https://techliberation.com/2023/02/09/studies-document-growing-cost-of-eu-privacy-regulations/ https://techliberation.com/2023/02/09/studies-document-growing-cost-of-eu-privacy-regulations/#comments Thu, 09 Feb 2023 16:22:47 +0000 https://techliberation.com/?p=77086

[Originally published on Medium on 2/5/2022]

In an earlier essay, I explored “Why the Future of AI Will Not Be Invented in Europe” and argued that, “there is no doubt that European competitiveness is suffering today and that excessive regulation plays a fairly significant role in causing it.” This essay summarizes some of the major academic literature that leads to that conclusion.

Since the mid-1990s, the European Union has been layering on highly restrictive policies governing online data collection and use. The most significant of the E.U.’s recent mandates is the 2018 General Data Protection Regulation (GDPR). This regulation established even more stringent rules related to the protection of personal data, the movement thereof, and limits what organizations can do with data. Data minimization is the major priority of this system, but there are many different types of restrictions and reporting requirements involved in the regulatory scheme. This policy framework also has ramifications for the future of next-generation technologies, especially artificial intelligence and machine learning systems, which rely on high-quality data sets to improve their efficacy.

Whether or not the E.U.’s complicated regulatory regime has actually resulted in truly meaningful privacy protections for European citizens relative to people in other countries remains open to debate. It is very difficult to measure and compare highly subjective values like privacy across countries and cultures. This makes benefit-cost analysis for privacy regulation extremely challenging — especially on the benefits side of the equation.

What is no longer up for debate, however, is the cost side of the equation and the question of what sort of consequences the GDPR has had on business formation, competition, investment, and so on. On these matters, standardized metrics exist and the economic evidence is abundantly clear: the GDPR has been a disaster for Europe.

Summary of Major Studies on Impact of EU Data Regulation

Consider the impact of E.U. data controls on business startups and market structure. GDPR and other regulations greatly limit the flow of data to innovative upstarts who need it most to compete, leaving only the largest companies who can afford to comply to control most of the market. Benjamin Mueller of ITIF notes that it is already the case that just “two of the world’s 30 largest technology firms by market capitalization are from the EU,” and only “5 of the 100 most promising AI startups are based in Europe,” while private funding of AI startups in Europe for 2020 ($4 billion) was dwarfed by US ($36 billion) and China ($25 billion). These issues are even more pressing as the E.U. looks to advance a new AI Act, which would layer on still more regulatory restrictions.

In concrete terms, this has meant that the E.U. came away from the digital revolution with “the complete absence of superstar companies,” argue competition policy experts Nicolas Petit and David Teece. There are no European versions of Microsoft, Google, or Apple, even though Europeans clearly demand the sort of products and services those US-based companies provide. Entrepreneurialism scholar Zoltan Acs asks: “What has been the outcome of E.U. policy in limiting entrepreneurial activity over recent decades?” His conclusion:

It is immediately clear… that the United States and China dominate the platform landscape. Based on the market value of top companies, the United States alone represents 66% of the world’s platform economy with 41 of the top 100 companies. European platform-based companies play a marginal role, with only 3% of market value.

Several recent studies have documented the costs associated with the GDPR and the E.U.’s heavy-handed approach to data flows more generally. Here is a rundown of some of the academic evidence and a summary of the major findings from these studies.

“There is a growing body of economic literature and commentary showing that the costs of implementing the GDPR benefit large online platforms, and that consent-based data collection gives a competitive advantage to firms offering a range of consumer-facing products compared to smaller market actors. This in turn increases concentration in a number of digital markets where access to data is important, by creating barriers to entry or encouraging market exit.” (p. 2–3)

“this paper examines how privacy regulation shaped firm performance in a large sample of companies across 61 countries and 34 industries. Controlling for firm and country-industry-year unobserved characteristics, we compare the outcomes of firms at different levels of exposure to EU markets, before and after the enforcement of the GDPR in 2018. We find that enhanced data protection had the unintended consequence of reducing the financial performance of companies targeting European consumers. Across our full sample, firms exposed to the regulation experienced a 8% decline in profits, and a 2% reduction in sales. An exception is large technology companies, which were relatively unaffected by the regulation on both performance measures. Meanwhile, we find the negative impact on profits among small technology companies to be almost double the average effect across our full sample. Following several robustness tests and placebo regressions, we conclude that the GDPR has had significant negative impacts on firm performance in general, and on small companies in particular.” (p. 1)

“We show that websites’ vendor use falls after the European Union’s General Data Protection Regulation (GDPR), but that market concentration also increases among technology vendors that provide support services to websites. We collect panel data on the web technology vendors selected by more than 27,000 top websites internationally. The week after the GDPR’s enforcement, website use of web technology vendors falls by 15% for EU residents. Websites are more likely to drop smaller vendors, which increases the relative concentration of the vendor market by 17%. Increased concentration predominantly arises among vendors that use personal data such as cookies, and from the increased relative shares of Facebook and Google-owned vendors, but not from website consent requests. Though the aggregate changes in vendor use and vendor concentration dissipate by the end of 2018, we find that the GDPR impact persists in the advertising vendor category most scrutinized by regulators. Our findings shed light on potential explanations for the sudden drop and subsequent rebound in vendor usage.” (p. 1)

“GDPR creates inherent tradeoffs between data protection and other dimensions of welfare, including competition and innovation. While some of these effects were acknowledged when constructing the legal data regime, many were disregarded. Furthermore, the magnitude and breadth of such effects may well constitute an unintended and unheeded welfare-reducing consequence. As this article shows, the GDPR limits competition and increases concentration in data and data-related markets, and potentially strengthens large data controllers. It also further reinforces the already existing barriers to data sharing in the EU, thereby potentially reducing data synergies that might result from combining different datasets controlled by separate entities.” (pp. 3–4)

“Using data on 4.1 million apps at the Google Play Store from 2016 to 2019, we document that GDPR induced the exit of about a third of available apps; and in the quarters following implementation, entry of new apps fell by half. We estimate a structural model of demand and entry in the app market. Comparing long-run equilibria with and without GDPR, we find that GDPR reduces consumer surplus and aggregate app usage by about a third. Whatever the privacy benefits of GDPR, they come at substantial costs in foregone innovation.”

“this paper empirically quantifies the effects of the enforcement of the EU’s General Data Protection Regulation (GDPR) on online user behavior over time, analyzing data from 6,286 websites spanning 24 industries during the 10 months before and 18 months after the GDPR’s enforcement in 2018. A panel differences estimator, with a synthetic control group approach, isolates the short- and long-term effects of the GDPR on user behavior. The results show that, on average, the GDPR’s effects on user quantity and usage intensity are negative; e.g., the numbers of total visits to a website decrease by 4.9% and 10% due to GDPR in respectively the short- and long-term. These effects could translate into average revenue losses of $7 million for e-commerce websites and almost $2.5 million for ad-based websites 18 months after GDPR. The GDPR’s effects vary across websites, with some industries even benefiting from it; moreover, more-popular websites suffer less, suggesting that the GDPR increased market concentration.”

“This paper investigates the impact of the General Data Protection Regulation (GDPR for short) on consumers’ online browsing and search behavior using consumer panels from four countries, United Kingdom, Spain, United States, and Brazil. We find that after GDPR, a panelist exposed to GDPR submits 21.6% more search terms to access information and browses 16.3% more pages to access consumer goods and services compared to a non-exposed panelist, indicating higher friction in online search. The implications of increased friction are heterogeneous across firms: Bigger e-commerce firms see an increase in consumer traffic and more online transactions. The increase in the number of transactions at large websites is about 6 times the increase experienced by smaller firms. Overall, the post-GDPR online environment may be less competitive for online retailers and may be more difficult for EU consumers to navigate through.”

“Privacy regulations should increase trust because they provide laws that increase transparency and allow for punishment in cases in which the trustee violates trust. […] We collected survey panel data in Germany around the implementation date and ran a survey experiment with a GDPR information treatment. Our observational and experimental evidence does not support the hypothesis that the GDPR has positively affected trust. This finding and our discussion of the underlying reasons are relevant for the wider research field of trust, privacy, and big data.”

“We follow more than 110,000 websites and their third-party HTTP requests for 12 months before and 6 months after the GDPR became effective and show that websites substantially reduced their interactions with web technology providers. Importantly, this also holds for websites not legally bound by the GDPR. These changes are especially pronounced among less popular websites and regarding the collection of personal data. We document an increase in market concentration in web technology services after the introduction of the GDPR: Although all firms suffer losses, the largest vendor — Google — loses relatively less and significantly increases market share in important markets such as advertising and analytics. Our findings contribute to the discussion on how regulating privacy, artificial intelligence and other areas of data governance relate to data minimization, regulatory competition, and market structure.”

William Rinehart of the Center for Growth and Opportunity has compiled and summarized many additional studies that document the costs associated with restrictions on data, including many state privacy laws imposed in the United States.

“The Biggest Loser”: Innovation Culture Gone Wrong

Taken together, this evidence makes it clear that, “Well-meaning privacy laws can have the unintended consequence of penalizing smaller companies within technology markets.” It can also have broader geopolitical ramifications for continental competitive advantage and engagement between countries. Some have argued that the United Kingdom’s so-called “Brexit” from the EU can be viewed as not only an effort to reclaim its sovereignty but more specifically “to escape its crippling regulatory structure.” The E.U.’s approach to emerging technology regulation likely had some bearing on this. Acs argues that Britain’s move was logical, “because E.U. regulations were holding back the U.K.’s strong DPE (digital platform economy).” “If the United Kingdom was to realize its economic potential,” he says, “it had to extricate itself from the European Union,” due to the growing “dysfunctional E.U. bureaucracy.”

Can Europe turn things around? Most market watchers do not believe that the E.U. will be willing to change its regulatory course in such a way that the continent would suddenly become more open to data-driven innovation. As part of a Spring 2022 journal symposium, The International Economy asked 11 experts from Europe and the U.S. to consider where the European Union currently stood in “the global tech race.” The responses were nearly unanimous and bluntly summarized in the symposium’s title: “The Biggest Loser.” Several respondents observed how “Europe is considered to be lagging behind in the global tech race,” and “is unlikely to become a global hub of innovation.” “The future will not be invented in Europe,” another respondent concluded. Europe’s risk-averse culture and preference for meticulously detailed and highly precautionary regulatory regimes were repeatedly cited as factors.

Europe has become the biggest loser on the digital technology front not because of their people but because of their policy. Europe is filled with some of the most important advanced education and engineering programs in the world, and countless brilliant minds there could be leading world-leading digital technology companies that could rival the U.S., China, and the rest of the world. But Europe’s current “innovation culture” simply will not allow it.

Innovation culture refers to “the various social and political attitudes and pronouncements towards innovation, technology, and entrepreneurial activities that, taken together, influence the innovative capacity of a culture or nation.” A positive innovation culture depends upon a dynamic, open economy that encourages new entry, entrepreneurialism, continuous investment, and the free movement of goods, ideas, and talent.

At this point in time, it is clear that — at least for data-driven sectors — the E.U. has created the equivalent of an anti-innovation culture, and the GDPR has clearly played a major rule in that outcome. This regulatory regime has also had devastating consequences for venture capital formation and investment more generally in Europe. “Public policy and attitudes explain the relative technological decline and lack of economic dynamism,” Petit and Teece argue, and it has resulted in, “weak venture capital markets, fragmented research capabilities, low worker mobility and frustrated entrepreneurs.”

Industrial Policy Won’t Save Europe

While the E.U. is aggressively regulating data-driven sectors, it is simultaneously trying to use industrial policy programs to advance new technological capabilities and innovations. European policymakers would obviously like to avoid a repeat of the past quarter century and the lack of digital technology competition and innovation they witnessed.

But past European industrial policy efforts on the digital technology front have largely failed, as Connor Haaland and I documented earlier. Zoltan Acs notes that, despite many state efforts to promote digital innovation across the continent in recent decades, the E.U.’s regulatory policies have resulted in the opposite. “The European Union protected traditional industries and hoped that existing firms would introduce new technologies. This was a policy designed to fail,” he argues. A major recent book, Questioning the Entrepreneurial State: Status-quo, Pitfalls, and the Need for Credible Innovation Policy (Springer, 2022), offers additional evidence of the failure of European industrial policy efforts. No amount of industrial policy planning and spending is going to be able to overcome a negative innovation culture that suffocates entrepreneurialism and investment out of the gates.

These findings have lessons for policymakers in the United States, too, especially with President Biden and even many Republicans now calling for heavy-handed top-down regulation of digital technology companies. Basically, “President Biden Wants America to Become Europe on Tech Regulation,” I argued in a recent R Street Institute blog post. In a letter to the Wall Street Journal, I responded to recent opeds by both President Biden and former Trump Administration Attorney General William Barr in which they both advocated regulations that would take us down the disastrous path that the European Union has already charted.

“The only thing Europe exports now on the digital-technology front is regulation,” I noted in my response, and that makes it all the more mind-boggling that Biden and Barr want to go down that same path. “Overregulation by EU bureaucrats led Europe’s best entrepreneurs and investors to flee to the U.S. or elsewhere in search of the freedom to innovate.” This is the wrong innovation culture for the United States if we hope to be a leader in the Computational Revolution that is unfolding — and match expanding efforts by the Chinese to top us at it.

In closing, policymakers should never lose sight of the most fundamental lesson of innovation policy, which can be stated quite simply: You only get as much innovation as you allow to begin with. If the public policy defaults are all set to be maximally restrictive and limit entrepreneurialism and experimentation by design, then it should be no surprise when the country or continent fails to generate meaningful innovation, investment, new companies, and global competitive advantage. The European model is no model for America.

Additional reading:

]]> https://techliberation.com/2023/02/09/studies-document-growing-cost-of-eu-privacy-regulations/feed/ 2 77086 Tech Regulation Will Increasingly Be Driven Through the Prism of “Algorithmic Fairness” https://techliberation.com/2022/11/06/tech-regulation-will-increasingly-be-driven-through-the-prism-of-algorithmic-fairness/ https://techliberation.com/2022/11/06/tech-regulation-will-increasingly-be-driven-through-the-prism-of-algorithmic-fairness/#comments Sun, 06 Nov 2022 18:51:21 +0000 https://techliberation.com/?p=77056

We are entering a new era for technology policy in which many pundits and policymakers will use “algorithmic fairness” as a universal Get Out of Jail Free card when they push for new regulations on digital speech and innovation. Proposals to regulate things like “online safety,” “hate speech,” “disinformation,” and “bias” among other things often raise thorny definitional questions because of their highly subjective nature. In the United States, efforts by government to control these things will often trigger judicial scrutiny, too, because restraints on speech violate the First Amendment. Proponents of prior restraint or even ex post punishments understand this reality and want to get around it. Thus, in an effort to avoid constitutional scrutiny and lengthy court battles, they are engaged in a rebranding effort and seeking to push their regulatory agendas through a techno-panicky prism of “algorithmic fairness” or “algorithmic justice.”

Hey, who could possibly be against FAIRNESS and JUSTICE? Of course, the devil is always in the details as Neil Chilson and I discuss in our new paper for the The Federalist Society and Regulatory Transparency Project on, “The Coming Onslaught of ‘Algorithmic Fairness’ Regulations.” We document how federal and state policymakers from both parties are currently considering a variety of new mandates for artificial intelligence (AI), machine learning, and automated systems that, if imposed, “would thunder through our economy with one of the most significant expansions of economic and social regulation – and the power of the administrative state – in recent history.”

We note how, at the federal level, bills are being floated with titles like the “Algorithmic Justice and Online Platform Transparency Act” and the “Protecting Americans from Dangerous Algorithms Act,” which would introduce far-reaching regulations requiring AI innovators to reveal more about how their algorithms work or even hold them liable if their algorithms are thought to be amplifying hateful or extremist content. Other proposed measures like the “Platform Accountability and Consumer Transparency Act” and the “Online Consumer Protection Act” would demand greater algorithmic transparency as it relates to social media content moderation policies and procedures. Finally, measures like the “Kids Online Safety Act” would require audits of algorithmic recommendation systems that supposed targeted or harmed children. Algorithmic regulation is also creeping into proposed privacy regulations, such as the “American Data Protection and Privacy Act of 2022.”

And then there are all the state laws–many of which have been pushed by conservatives–that would mandate “algorithmic transparency” for social media content moderation in the name of countering supposed viewpoint bias. Bills in Florida and Texas take this approach. Meanwhile, conservatives in Congress Senator Josh Hawley’s (R-MO) push for bills like the “Ending Support for Internet Censorship Act” that requires large tech companies undergo external audits proving that their algorithms and content-moderation techniques are politically unbiased. It’s an open invitation to regulators and trial lawyers to massively regulate technology and speech under the guise of “algorithmic fairness.” Countless left-leaning law professors and European officials have already proposed a comprehensive algorithmic audit apparatus to regulate innovators in every sector.

It’s the rise of the Code Cops. If we continue down this path, it ends with a complete rejection of the permissionless innovation ethos that made America’s information technology sector a global powerhouse. Instead, we’ll be stuck with the very worst type of “Mother, May I” precautionary principle-based regulatory regime that will be imposing the equivalent of occupational licensing requirements for coders.

If code is speech, algorithms are as well. Defenders of innovation freedom need to step up and prepare for the fight to come. [See my earlier essay, “AI Eats the World: Preparing for the Computational Revolution and the Policy Debates Ahead.”] Chilson and I outline the broad contours of the battle for freedom of speech and the freedom to innovation that is brewing. It will be the most important technology policy issue of the next ten years. I hope you take the time to read our new essay and understand why. And below you will find a few dozen more essay on the same topic if you’d like to dig even deeper.

Additional Reading :

 

]]> https://techliberation.com/2022/11/06/tech-regulation-will-increasingly-be-driven-through-the-prism-of-algorithmic-fairness/feed/ 4 77056 AI Eats the World: Preparing for the Computational Revolution and the Policy Debates Ahead https://techliberation.com/2022/09/12/ai-eats-the-world-preparing-for-the-computational-revolution-and-the-policy-debates-ahead/ https://techliberation.com/2022/09/12/ai-eats-the-world-preparing-for-the-computational-revolution-and-the-policy-debates-ahead/#comments Mon, 12 Sep 2022 23:52:26 +0000 https://techliberation.com/?p=77039

[Cross-posted from Medium.]

The Coming Computational Revolution

Thomas Edison once spoke of how electricity was a “field of fields.” This is even more true of AI, which is ready to bring about a sweeping technological revolution. In Carlota Perez’s influential 2009 paper on “Technological Revolutions and Techno-economic Paradigms,” she defined a technological revolution “as a set of interrelated radical breakthroughs, forming a major constellation of interdependent technologies; a cluster of clusters or a system of systems.” To be considered a legitimate technological revolution, Perez argued, the technology or technological process must be “opening a vast innovation opportunity space and providing a new set of associated generic technologies, infrastructures and organisational principles that can significantly increase the efficiency and effectiveness of all industries and activities.” In other words, she concluded, the technology must have “the power to bring about a transformation across the board.”

Expanding Our Skillset

Thus, AI (and AI policy) is multi-dimensional, amorphous, and ever-changing. It has many layers and complexities. This will require public policy analysts and institutions to reorient their focus and develop new capabilities.

Mapping the AI Policy Terrain: Broad vs. Narrow

Beyond talent development, the other major challenge is issue coverage. How can we cover all the AI policy bases? There are two general categories of AI concerns, and supporters of free markets need to be prepared to engage on both battlefields.

Confronting the Formidable Resistance to Change

Finally, free-market analysts and organizations must prepare to defend the general concept of progress through technological change as AI becomes a central social, economic, and legal battleground — both domestically and globally. Every technological revolution involves major social and economic disruptions and gives rise to intense efforts to defend the status quo and block progress. As Perez concludes, “the profound and wide-ranging changes made possible by each technological revolution and its techno-economic paradigm are not easily assimilated; they give rise to intense resistance.”

]]> https://techliberation.com/2022/09/12/ai-eats-the-world-preparing-for-the-computational-revolution-and-the-policy-debates-ahead/feed/ 3 77039 AI Governance “on the Ground” vs “on the Books” https://techliberation.com/2022/08/24/ai-governance-on-the-ground-vs-on-the-books/ https://techliberation.com/2022/08/24/ai-governance-on-the-ground-vs-on-the-books/#respond Wed, 24 Aug 2022 15:14:56 +0000 https://techliberation.com/?p=77028

[Cross-posted from Medium]

There are two general types of technological governance that can be used to address challenges associated with artificial intelligence (AI) and computational sciences more generally. We can think of these as “on the ground” (bottom-up, informal “soft law”) governance mechanisms versus “on the books” (top-down, formal “hard law”) governance mechanisms.

Unfortunately, heated debates about the latter type of governance often divert attention from the many ways in which the former can (or already does) help us address many of the challenges associated with emerging technologies like AI, machine learning, and robotics. It is important that we think harder about how to optimize these decentralized soft law governance mechanisms today, especially as traditional hard law methods are increasingly strained by the relentless pace of technological change and ongoing dysfunctionalism in the legislative and regulatory arenas.

On the Grounds vs. On the Books Governance

Let’s unpack these “on the ground” and “on the books” notions a bit more. I am borrowing these descriptors from an important 2011 law review article by Kenneth A. Bamberger and Deirdre K. Mulligan, which explored the distinction between what they referred to as “Privacy on the Books and on the Ground.” They identified how privacy best practices were emerging in a decentralized fashion thanks to the activities of corporate privacy officers and privacy associations who helped formulate best practices for data collection and use.

The growth of privacy professional bodies and non­profit organizations — especially the International Association of Privacy Profession­als (IAPP) — helped better formalize privacy best practices by establishing and certifying internal champions to uphold key data-handling principles with organizations. By 2019, the IAPP had over 50,000 trained members globally, and its numbers keep swelling. Today, it is quite common to find Chief Privacy Officers throughout the corporate, governmental, and non-profit world.

These privacy professionals work together and in conjunction with a wide diversity of other players to “bake-in” widely-accepted information collection/ use practices within all these organizations. With the help of IAPP and other privacy advocates and academics, these professionals also look to constantly refine and improve their standards to account for changing circumstances and challenges in our fast-paced data economy. They also look to ensure that organizations live up to commitments they have made to the public or even governments to abide by various data-handling best practices.

Soft Law vs. Hard Law

These “on the ground” efforts have helped usher in a variety of corporate social responsibility best practices and provide a flexible governance model that can be a compliment to, or sometimes even a substitute for, formal “on the books” efforts. We can also think of this as the difference between soft law and hard law.

Soft law refers to agile, adaptable governance schemes for emerging technology that create substantive expectations and best practices for innovators without regulatory mandates. Soft law can take many forms, including guidelines, best practices, agency consultations & workshops, multistakeholder initiatives, and other experimental types of decentralized, non-binding commitments and efforts.

Soft law has become a bit of a gap-filler in the U.S. as hard law efforts fail for various reasons. The most obvious explanations for why the role of hard law governance has shrunk is that it’s just very hard for law to keep up with fast-moving technological developments today. This is known as the pacing problem. Many scholars have identified how the pacing problem gives rise to a “governance gap” or “competency trap” for policymakers because, just as quickly as they are coming to grips with new technological developments, other technologies are emerging quickly on their heels.

Think of modern technologies — especially informational and computational technologies — like a series of waves that come flowing in to shore faster and faster. As soon as one wave crests and then crashes down, another one comes right after it and soaks you again before you’ve had time to recover from the daze of the previous ones hitting you. In a world of combinatorial innovation, in which technologies build on top of one another in a symbiotic fashion, this process becomes self-reinforcing and relentless. For policymakers, this means that just when they’ve worked their way up one technological learning curve, the next wave hits and forces them to try to quickly learn about and prepare for the next one that has arrived. Lawmakers are often overwhelmed by this flood of technological change, making it harder and harder for policies to get put in place in a timely fashion — and equally hard to ensure that any new or even existing policies stay relevant as all this rapid-fire innovation continues.

Legislative dysfunctionalism doesn’t help. Congress has a hard time advancing bills on many issues, and technical matters often get pushed to the bottom of the priorities list. The end result is that Congress has increasingly become a non-actor on tech policy in the U.S. Most of the action lies elsewhere.

What’s Your Backup Plan?

This means there is a powerful pragmatic case for embracing soft law efforts that can at least provide us with some “on the ground” governance efforts and practices. Increasingly, soft law is filling the governance gap because hard law is failing for a variety of reasons already identified. Practically speaking, even if you are dead set on imposing a rigid, top-down, technocratic regulatory regime on any given sector or technology, you should at least have a backup plan in mind if you can’t accomplish that.

This is why privacy governance in the United States continues to depend heavily on such soft law efforts to fill the governance vacuum after years of failed attempts to enact a formal federal privacy law. While many academics and others continue to push for such an over-arching data handling law, bottom-up soft law efforts have played an important role in balancing privacy and innovation.

In a similar way, “on the ground” governance efforts are already flourishing for artificial intelligence and machine learning as policymakers continue to very slowly consider whether new hard law initiatives are wise or even possible. For example, congressional lawmakers have been considering a federal regulatory framework for driverless cars for the past several sessions of Congress. Many people in Congress and in academic circles agree that a federal framework is needed, if for no other reason than to preempt the much-dreaded specter of a patchwork of inconsistent state and local regulatory policies. With so much bipartisan agreement out there on driverless car legislation, it would seem like a federal bill would be a slam dunk. For that reason, year in and year out, people always predict: this is the year we’ll get driverless car legislation! And yet, it never happens due to a combination of special interest opposition from unions and trial lawyers, in addition to the pacing problem issue and Congress focusing its limited attention on other issues.

This is also already true for algorithmic regulation. We hear lots of calls to do something, but it remains unclear what that something is or whether it will get done any time soon. If we could not get a privacy bill through Congress after at least a dozen years of major efforts, chances are that broad-based AI regulation is going to be equally challenging.

Soft Law for AI is Exploding

Thus, soft law will likely fill the governance gap for AI. It already is. I’m working on a new book that documents the astonishing array of soft law mechanisms already in place or being developed to address various algorithmic concerns. I can’t seem to finish the book because there is just so much going on related to soft law governance efforts for algorithmic systems. As Mark Coeckelbergh noted in his recent book on AI Ethics, there’s been an “avalanche of​ initiatives and policy documents” around AI ethics and best practices in recent years. It is a bit overwhelming, but the good news is that there is a lot of consistency in these governance efforts.

To illustrate, a 2019 survey by a group of researchers based in Switzerland analyzed 84 AI ethical frameworks and found “a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy).” A more recent 2021 meta-survey by a team of Arizona State University (ASU) legal scholars reviewed an astonishing 634 soft law AI programs that were formulated between 2016–2019. 36 percent of these efforts were initiated by governments, with the others being led by non-profits or private sector bodies. Echoing the findings from the Swiss researchers, the ASU report found widespread consensus among these soft law frameworks on values such as transparency and explainability, ethics/rights, security, and bias. This makes it clear that there is considerable consistency among ethical soft law frameworks in that most of them focus on a core set of values to embed within AI design. The UK-based Alan Turing Institute boils their list down to four “FAST Track Principles”: Fairness, Accountability, Sustainability, and Transparency.

The ASU scholars noted how ethical best practices for product design already influence developers today by creating powerful norms and expectations about responsible product design. “Once a soft law program is created, organizations may seek to enforce it by altering how their employees or representatives perform their duties through the creation and implementation of internal procedures,” they note. “Publicly committing to a course of action is a signal to society that generates expectations about an organization’s future actions.”

This is important because many major trade associations and individual companies have been formulating governance frameworks and ethical guidelines for AI development and use. For example, among large trade associations, the U.S. Chamber of Commerce, the Business Roundtable, the BSA | The Software Alliance, and ACT (The App Association) have all recently released major AI best practice guidelines. Notable corporate efforts to adopt guidelines for ethical AI practices include statements or frameworks by IBM, Intel, Google, Microsoft, Salesforce, SAP, and Sony, to just name a few. They are also creating internal champions to push AI ethics though either the appointment of Chief Ethical Officers, the creation of official departments, or both plus additional staff to guide the process of baking-in AI ethics by design.

Once again, there is remarkable consistency among these corporate statements in terms of the best practices and ethical guidelines they endorse. Each trade association or corporate set of guidelines align closely with the core values identified in the hundreds of other soft law frameworks that ASU scholars surveyed. These efforts go a long way toward helping to promote a culture of responsibility among leading AI innovators. We can think of this as the professionalization of AI best practices.

What Soft Law Critics Forget

Some will claim that “on the ground” soft law efforts are not enough, but they typically make two mistakes when saying so.

Their first mistake is thinking that hard law is practical or even optimal for fast-paced, highly mercurial AI and ML technologies. It’s not just that the pacing problem necessitates new thinking about governance. Critics fail to understand how hard law would likely significantly undermine algorithmic innovation because algorithmic systems can change by the minute and require a more agile and adaptive system of governance by their very nature.

This is a major focus of my book and I previously published a draft chapter from my book on “The Proper Governance Default for AI,” and another essay on “Why the Future of AI Will Not Be Invented in Europe.” These essays explain why a Precautionary Principle-oriented regulatory regime for algorithmic systems would stifle technological development, undermine entrepreneurialism, diminish competition and global competitive advantage, and even have a deleterious impact on our national security goals.

Traditional regulatory systems can be overly rigid, bureaucratic, inflexible, and slow to adapt to new realities. They focus on preemptive remedies that aim to predict the future, and future hypothetical problems that may not ever come about. Worse yet, administrative regulation generally preempts or prohibits the beneficial experiments that yield new and better ways of doing things. When innovators must seek special permission before they offer a new product or service, it raises the cost of starting a new venture and discourages activities that benefit society. We need to avoid that approach if we hope maximize the potential of AI-based technologies.

The second mistake that soft law critics make is that they fail to understand how many hard law mechanisms actually play a role in supporting soft law governance. AI applications already are regulated by a whole host of existing legal policies. If someone does something stupid or dangerous with AI systems, the Federal Trade Commission (FTC) has the power to address “unfair and deceptive practices” of any sort. And state Attorneys General and state consumer protection agencies also routinely address unfair practices and continue to advance their own privacy and data security policies, some of which are often more stringent than federal law.

Meanwhile, several existing regulatory agencies in the U.S. possess investigatory and recall authority that allows them to remove products from the market when certain unforeseen problems manifest themselves. For example, the National Highway Traffic Safety Administration (NHTSA), the Food & Drug Administration (FDA), and Consumer Product Safety Commission (CPSC) all possess broad recall authority that could be used to address risks that develop for many algorithmic or robotic systems. For example, NHTSA is currently using its investigative authority to evaluate Tesla’s claims about “full self-driving” technology and the agency has the power to take action against the company under existing regulations. Likewise, the FDA used its broad authority to crack down on genetic testing company 23andme many years ago. And CPSC and the FTC have broad authority to investigate claims made by innovators, and they’ve already used it. It’s not like our expansive regulatory state lacks considerable existing power to police new technology. If anything, the power of the administrative state is too broad and amorphous and it can be abused in certain instances.

Perhaps most importantly, our common law system can address other deficiencies with AI-based systems and applications using product defects law, torts, contract law, property law, and class action lawsuits. This is a better way of addressing risks compared to preemptive regulation of general-purpose AI technology because it at least allows the technologies to first develop and then see what actual problems manifest themselves. Better to treat innovators as innocent until proven guilty than the other way around.

There are other thorny issues that deserve serious policy consideration and perhaps even some new rules. But how risks are addressed matters deeply. Before we resort to heavy-handed, legalistic solutions for possible problems, we should exhaust all other potential remedies first.

In other words, “on the ground” soft law government mechanisms and ex post legal solutions should generally trump “ex ante (preemptive, precautionary) regulatory constraints. But we should look for ways to refine and improve soft law governance tools, perhaps through better voluntary certification and auditing regimes to hold developers to a high standard as it pertains to the important AI ethical practices we want them to uphold. This is the path forward to achieve responsible AI innovation without the heavy-handed baggage associated with more formalistic, inflexible, regulatory approaches that are ill-suited for complicated, rapidly-evolving computational and computing technologies.

___________________

Related Reading on AI & Robotics

]]> https://techliberation.com/2022/08/24/ai-governance-on-the-ground-vs-on-the-books/feed/ 0 77028 Running List of My Research on AI, ML & Robotics Policy https://techliberation.com/2022/07/29/running-list-of-my-research-on-ai-ml-robotics-policy/ https://techliberation.com/2022/07/29/running-list-of-my-research-on-ai-ml-robotics-policy/#respond Fri, 29 Jul 2022 12:51:54 +0000 https://techliberation.com/?p=77020

[last updated 4/3/2025 – Check my Medium page for latest posts]

This a running list of all the essays and reports I’ve already rolled out on the governance of artificial intelligence (AI), machine learning (ML), and robotics. Why have I decided to spend so much time on this issue? Because this will become the most important technological revolution of our lifetimes. Every segment of the economy will be touched in some fashion by AI, ML, robotics, and the power of computational science. It should be equally clear that public policy will be radically transformed along the way.

Eventually, all policy will involve AI policy and computational considerations. As AI “eats the world,” it eats the world of public policy along with it. The stakes here are profound for individuals, economies, and nations. As a result, AI policy will be the most important technology policy fight of the next decade, and perhaps next quarter century. Those who are passionate about the freedom to innovate need to prepare to meet the challenge as proposals to regulate AI proliferate.

There are many socio-technical concerns surrounding algorithmic systems that deserve serious consideration and appropriate governance steps to ensure that these systems are beneficial to society. However, there is an equally compelling public interest in ensuring that AI innovations are developed and made widely available to help improve human well-being across many dimensions. And that’s the case that I’ll be dedicating my life to making in coming years.

Here’s the list of what I’ve done so far. I will continue to update this as new material is released:

2025

2024

2023

2022

2021 (and earlier)

]]> https://techliberation.com/2022/07/29/running-list-of-my-research-on-ai-ml-robotics-policy/feed/ 0 77020 My Forthcoming Book on Artificial Intelligence & Robotics Policy https://techliberation.com/2022/07/22/my-forthcoming-book-on-artificial-intelligence-robotics-policy/ Fri, 22 Jul 2022 18:13:14 +0000 https://techliberation.com/?p=77014

I’m finishing up my next book, which is tentatively titled, “A Flexible Governance Framework for Artificial Intelligence.” I thought I’d offer a brief preview here in the hope of connecting with others who care about innovation in this space and are also interested in helping to address these policy issues going forward.

The goal of my book is to highlight the ways in which artificial intelligence (AI) machine learning (ML), robotics, and the power of computational science are set to transform the world—and the world of public policy—in profound ways. As with all my previous books and research products, my goal in this book includes both empirical and normative components. The first objective is to highlight the tensions between emerging technologies and the public policies that govern them. The second is to offer a defense of a specific governance stance toward emerging technologies intended to ensure we can enjoy the fruits of algorithmic innovation.

AI is a transformational technology that is general-purpose and dual-use. AI and ML also build on top of other important technologies—computing, microprocessors, the internet, high-speed broadband networks, and data storage/processing systems—and they will become the building blocks for a great many other innovations going forward. This means that, eventually, all policy will involve AI policy and computational considerations at some level. It will become the most important technology policy issue here and abroad going forward.

The global race for AI supremacy has important implications for competitive advantage and other geopolitical issues. This is why nations are focusing increasing attention on what they need to do to ensure they are prepared for this next major technological revolution. Public policy attitudes and defaults toward innovative activities will have an important influence on these outcomes.

In my book, I argue that, if the United States hopes to maintain a global leadership position in AI, ML, and robotics, public policy should be guided by two objectives:

  1. Maximize the potential for innovation, entrepreneurialism, investment, and worker opportunities by seeking to ensure that firms and other organizations are prepared to compete at a global scale for talent and capital and that the domestic workforce is properly prepared to meet the same global challenges.
  2. Develop a flexible governance framework to address various ethical concerns about AI development or use to ensure these technologies benefit humanity, but work to accomplish this goal without undermining the goals set forth in the first objective.

The book primarily addresses the second of these priorities because getting the governance framework for AI right significantly improves the chances of successfully accomplishing the first goal of ensuring that the United States remains a leading global AI innovator.

I do a deep dive into the many different governance challenges and policy proposals that are floating out there today—both domestically and internationally. The most contentious of these issues involved the so-called “socio-algorithmic” concerns that are driving calls for comprehensive regulation today. Those include the safety, security, privacy, and discrimination risks that AI/ML technologies could pose for individuals and society.

These concerns deserve serious consideration and appropriate governance steps to ensure that these systems are beneficial to society. However, there is an equally compelling public interest in ensuring that AI innovations are developed and made widely available to help improve human well-being across many dimensions.

Getting the balance right requires agile governance strategies and decentralized, polycentric approaches. There are many different values and complex trade-offs in play in these debates, all of which demand tailored responses. But this should not be done in an overly rigid way through complicated, inflexible, time-consuming regulatory mandates that preemptively curtail or completely constrain innovation opportunities. There’s no need to worry about the future if we can’t even build it first. AI innovation must not be treated as guilty until proven innocent.

The more agile and adaptive governance approach I outline in my book builds on the core principles typically recommended by those favoring precautionary principle-based regulation. That is, it is similarly focused on (1) “baking in” best practices and aligning AI design with widely-shared goals and values; and, (2) keeping humans “in the loop” at critical stages of this process to ensure that they can continue to guide and occasionally realign those values and best practices as needed. However, a decentralized governance approach to AI focuses on accomplishing these objectives in a more flexible, evolutionary fashion without the costly baggage associated with precautionary principle-based regulatory regimes.

The key to the decentralized approach is a diverse toolkit of so-called soft law governance solutions. Soft law refers to agile, adaptable governance schemes for emerging technology that create substantive expectations and best practices for innovators without regulatory mandates. Precautionary regulatory restraints will be necessary in some limited circumstances—particular for certain types of very serious existential risk—but most AI innovations should be treated as innocent until proven guilty.

When things do go wrong, many existing remedies are available, including a wide variety of common law solutions (torts, class actions, contract law, etc), recall authority possessed by many regulatory agencies, and various consumer protection policies and other existing laws. Moreover, the most effective solution to technological problems usually lies in more innovation, not less of it. It is only through constant trial and error that humanity discovers better and safer ways of satisfying important wants and needs.

The book has six chapters currently, although I am toying with adding back in two other chapters (on labor market issues and industrial policy proposals) that I finished but then cut to keep the theme of the book more tightly focused on social and ethical considerations surrounding AI and robotics.

Here are the summaries of the current six chapters in the manuscript:

  • Chapter 1: Understanding AI & Its Potential Benefits – Defining the nature and scope of artificial intelligence and its many components and related subsectors is complicated and this fact creates many governance challenges. But getting AI governance right is vital because these technologies offer individuals and society meaningful improvements in living standards across multiple dimensions.
  • Chapter 2: The Importance of Policy Defaults for Innovation Culture – Every technology policy debate involves a choice between two general defaults: the precautionary principle and the proactionary principle or “permissionless innovation.” Setting the initial legal default for AI technologies closer to the green light of permissionless innovation will enable greater entrepreneurialism, investment, and global competitiveness.
  • Chapter 3: Decentralized Governance for AI: A Framework – The process of embedding ethics in AI design is an ongoing, iterative process influenced by many forces and factors. There will be much trial and error when devising ethical guidelines for AI and hammering out better ways of keeping these systems aligned with human values. A top-down, one-size-fits-all regulatory framework for AI is unwise. A more decentralized, polycentric governance approach is needed—nationally and globally. [This chapter is the meat of the book and several derivative articles will be spun out of it beginning with a report on algorithmic auditing and AI impact assessments.]
  • Chapter 4: The US Governance Model for AI So Far – U.S. digital technology and ecommerce sectors have enjoyed a generally “permissionless” policy environment since the early days of the Internet, and this has greatly benefited our innovation and global competitiveness. While AI has thus far been governed by a similar “light-touch” approach, many academics and policymakers are now calling for aggressive regulation of AI rooted in a precautionary principle-oriented mindset, which threatens to derail a great deal of AI innovation.
  • Chapter 5: The European Regulatory Model & the Costs of Precaution by Default – Over the past quarter century, the European Union has taken a more aggressive approach to digital technology and data regulation, and is now advancing several new comprehensive regulatory frameworks, including an AI Act. The E.U.’s heavy-handed regulatory regime, which is rooted in the precautionary principle, discouraged innovation and investment across the continent in the past and will continue to do so as it grows to encompass AI technologies. The U.S. should reject this model and welcome European innovators looking to escape it.
  • Chapter 6: Existential Risks & Global Governance Issues around AI & Robotics – AI and robotics could give rise to certain global risks that warrant greater attention and action. But policymakers must be careful to define existential risk properly and understand how it is often the case that the most important solution to such risks is more technological innovation to overcome those problems. The greatest existential risk of all would be to block further technological innovation and scientific progress. Proposals to impose global bans or regulatory agencies are both unwise and unworkable. Other approaches, including soft law efforts, will continue to play a role in addressing global AI risks and concerns.

This book, which I hope to have out some time later this year, grows out of a large body of research I’ve done over the past decade. [Some of that work is listed down below.] AI, ML, robotics, and algorithmic policy issues will dominate my research focus and outputs over the next few years.

I look forward to doing my small part to help ensure that America builds on the track record of success it has enjoyed with the Internet, ecommerce, and digital technologies. Again, that stunning success story was built on wise policy choices that promoted a culture of creativity and innovation and rejected calls to hold on to past technological, economic, or legal status quos.

Will America rise to the challenge once again by adopting wise policies to facilitate the next great technological revolution? I’m ready for that fight. I hope you are, too, because it will be the most important technology policy battle of our lifetimes.

___________

Recent Essays & Papers on AI & Robotics Policy

]]> 77014 America Shouldn’t Follow EU’s Lead on AI Regulation https://techliberation.com/2022/07/22/america-shouldnt-follow-eus-lead-on-ai-regulation/ https://techliberation.com/2022/07/22/america-shouldnt-follow-eus-lead-on-ai-regulation/#comments Fri, 22 Jul 2022 15:42:08 +0000 https://techliberation.com/?p=77012

For my latest regular column in The Hill, I took a look at the trade-offs associated with the EU’s AI Act. This is derived from a much longer chapter on European AI policy that is in my forthcoming book, and I also plan on turning it into a free-standing paper at some point soon. My oped begins as follows:

In the intensifying race for global competitiveness in artificial intelligence (AI), the United States, China and the European Union are vying to be the home of what could be the most important technological revolution of our lifetimes. AI governance proposals are also developing rapidly, with the EU proposing an aggressive regulatory approach to add to its already-onerous regulatory regime. It would be imprudent for the U.S. to adopt Europe’s more top-down regulatory model, however, which already decimated digital technology innovation in the past and now will do the same for AI. The key to competitive advantage in AI will be openness to entrepreneurialism, investment and talent, plus a flexible governance framework to address risks.

Jump over to The Hill to read the entire thing. And down below you will find all my recent writing on AI and robotics. This will be my primary research focus in coming years.

Additional Reading :

]]> https://techliberation.com/2022/07/22/america-shouldnt-follow-eus-lead-on-ai-regulation/feed/ 6 77012 Event Video on Algorithmic Auditing and AI Impact Assessments https://techliberation.com/2022/07/13/event-video-on-algorithmic-auditing-and-ai-impact-assessments/ https://techliberation.com/2022/07/13/event-video-on-algorithmic-auditing-and-ai-impact-assessments/#comments Wed, 13 Jul 2022 18:10:03 +0000 https://techliberation.com/?p=77008

Upsides:

  • Audits and impact assessments can help ensure organizations live up their promises as it pertains to “baking in” ethical best practices (on issues like safety, security, privacy, and non-discrimination).
  • Audits and impact assessments are already utilized in other fields to address safety practices, financial accountability, labor practices and human rights issues, supply chain practices, and various environmental concerns.
  • Internal auditing / Institute of Internal Auditors (IIA) efforts could expand to include AI risks
  • Eventually, more and more organizations will expand their internal auditing efforts to incorporate AI risks because it makes good business sense to stay on top of these issues and avoid liability, negative publicity, or other customer backlash.
  • the International Association of Privacy Professionals (IAPP) trains and certifies privacy professionals through formal credentialing programs, supplemented by regular meetings, annual awards, and a variety of outreach and educational initiatives.
  • We should use similar model for AI and start by supplementing Chief Privacy Officers with Chief Ethical Officers.
  • This is how we formalize the ethical frameworks and best practices that have been formulated by various professional associations such as IEEE, ISO, ACM and others.
  • OECD — Framework for the Classification of AI Systems with the twin goals of helping “to develop a common framework for reporting about AI incidents that facilitates global consistency and interoperability in incident reporting,” and advancing “related work on mitigation, compliance and enforcement along the AI system lifecycle, including as it pertains to corporate governance.”
  • NIST — AI Risk Management Framework “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”
  • These frameworks being developed through a consensus-driven, open, transparent, and collaborative process. Not through top-down regulation.
  • Many AI developers and business groups have endorsed the use of such audits and assessments. BSA|The Software Alliance has said that, “By establishing a process for personnel to document key design choices and their underlying rationale, impact assessments enable organizations that develop or deploy high-risk AI to identify and mitigate risks that can emerge throughout a system’s lifecycle.”
  • Developers can still be held accountable for violations of certain ethical norms and bast practices both through private and potentially even through formal sanctions by consumer protection agencies (Federal Trade Commission / comparable state offices / by state AGs).
  • EqualAI / WEF — “Badge Program for Responsible AI Governance”
  • field of algorithmic consulting continues to expand (ex: O’Neil Risk Consulting)

Downsides:

  • constitutes a harm or impact in any given context will often be a contentious matter.
  • Auditing algorithms is nothing like auditing an accounting ledger, where the numbers either add up or they don’t.
  • With algorithms there are no binary metrics that can quantify the correct amount of privacy, safety, or security in any given system.
  • E.U. AI act will be a disaster for AI innovation and investment
  • Proposed U.S. Algorithmic Accountability Act of 2022 would require that developers perform impact assessments and file them with the Federal Trade Commission. A new Bureau of Technology would be created inside the agency to oversee the process.
  • If enforced through a rigid regulatory regime and another federal bureaucracy, compliance with algorithmic auditing mandates would likely become a convoluted, time-consuming bureaucratic process. That would likely slow the pace of AI development significantly.
  • Academic literature on AI auditing / impact assessment ignores potential costs; Mandatory auditing and assessments are treated as a sort of frictionless nirvana when we already know that such a process would entire significant costs.
  • Some AI scholars suggest that NEPA should be model for AI impact assessments / audits.
  • NEPA assessments were initially quite short (sometimes less than 10 pages), but today the average length of these statements is more than 600 pages and include appendices that average over 1,000 pages on top of that.
  • NEPA assessments take an average of 4.5 years to complete and that, between 2010 and 2017, there were four assessments that took at least 17 years to complete.
  • Many important public projects never get done or take far too long to complete at considerably higher expenditure than originally predicted.
  • would create a number of veto points that opponents of AI could use to stop much progress in the field. This is the “vetocracy” problem.
  • We cannot wait years or even months for bureaucracies to eventually getting around to formally signing off on audits or assessments, many of which would be obsolete before they were even done.
  • “global innovation arbitrage” problem would kick in: Innovators and investors increasingly relocate to the jurisdictions where they are treated most hospitably.
  • Both parties already accuse digital technology companies of manipulating their algorithms to censor their views.
  • Whichever party is in power at any given time could use the process to politicize terms like “safety,” “security,” and “non-discrimination” to nudge or even force private AI developers to alter their algorithms to satisfy the desires of partisan politicians or bureaucrats.
  • FCC abused its ambiguous authority to regulate “in the public interest” and indirectly censor broadcasters through intimidation via jawboning tactics and other “agency threats.” or “regulation by raised eyebrow”
  • There are potentially profound First Amendment issues in play with the regulation of algorithms that have not been explored here but which could become a major part of AI regulatory efforts going forward.

Summary:

  • Auditing and impact assessments can be a part of a more decentralized, polycentric governance framework.
  • Even in the absence of any sort of hard law mandates, algorithmic auditing and impact reviews represent an important way to encourage responsible AI development.
  • But we should be careful about mandating such things due to the many unanticipated cost and consequences of converting this into a top-down, bureaucratic regulatory regime.
  • The process should evolve gradually and organically, as it has in many other fields and sectors.
]]> https://techliberation.com/2022/07/13/event-video-on-algorithmic-auditing-and-ai-impact-assessments/feed/ 3 77008 The Proper Governance Default for AI https://techliberation.com/2022/05/26/the-proper-governance-default-for-ai/ https://techliberation.com/2022/05/26/the-proper-governance-default-for-ai/#comments Thu, 26 May 2022 20:15:21 +0000 https://techliberation.com/?p=76994

[This is a draft of a section of a forthcoming study on “A Flexible Governance Framework for Artificial Intelligence,” which I hope to complete shortly. I welcome feedback. I have also cross-posted this essay at Medium.]

Debates about how to embed ethics and best practices into AI product design is where the question of public policy defaults becomes important. To the extent AI design becomes the subject of legal or regulatory decision-making, a choice must be made between two general approaches: the precautionary principle or the proactionary principle.[1] While there are many hybrid governance approaches in between these two poles, the crucial issue is whether the initial legal default for AI technologies will be set closer to the red light of the precautionary principle (i.e., permissioned innovation) or to the green light of the proactionary principle (i.e., (permissionless innovation). Each governance default will be discussed.

The Problem with the Precautionary Principle as the Policy Default for AI

The precautionary principle holds that innovations are to be curtailed or potentially even disallowed until the creators of those new technologies can prove that they will not cause any theoretical harms. The classic formulation of the precautionary principle can be found in the “Wingspan Statement,” which was formulated at an academic conference that took place at the Wingspread Conference Center in Wisconsin in 1998. It read: “Where an activity raises threats of harm to the environment or human health, precautionary measures should be taken even if some cause and effect relationships are not fully established scientifically.”[2] There have been many reformulations of the precautionary principle over time but, as legal scholar Cass Sunstein has noted, “in all of them, the animating idea is that regulators should take steps to protect against potential harms, even if causal chains are unclear and even if we do not know that those harms will come to fruition.”[3] Put simply, under almost all varieties of the precautionary principle, innovation is treated as “guilty until proven innocent.”[4] We can also think of this as permissioned innovation.

The logic animating the precautionary principle reflects a well-intentioned desire to play it safe in the face of uncertainty. The problem lies in the way this instinct gets translated into law and regulation. Making the precautionary principle the public policy default for any given technology or sector has a strong bearing on how much innovation we can expect to flow from it. When trial-and-error experimentation is preemptively forbidden or discouraged by law, it can limit many of the positive outcomes that typically accompany efforts by people to be creative and entrepreneurial. This can, in turn, give rise to different risks for society in terms of forgone innovation, growth, and corresponding opportunities to improve human welfare in meaningful ways.

St. Thomas Aquinas once observed that if the sole goal of a captain were to preserve their ship, the captain would keep it in port forever. But that clearly is not the captain’s highest goal. Aquinas was making a simple but powerful point: There can be no reward without some effort and even some risk-taking. Ship captains brave the high seas because they are in search of a greater good, such as recognition, adventure, or income. Keeping ships in port forever would preserve their vessels, but at what cost?

Similarly, consider the wise words of Wilbur Wright, who pioneered human flight. Few people better understood the profound risks associated with entrepreneurial activities. After all, Wilbur and his brother were trying to figure out how to literally lift humans off the Earth. The dangers were real, but worth taking. “If you are looking for perfect safety,” Wright said, “you would do well to sit on a fence and watch the birds.” Humans would have never taken to the skies if the Wright brothers had not gotten off the fence and taken the risks they did. Risk-taking drives innovation and, over the long-haul, improves our well-being.[5] Nothing ventured, nothing gained.

These lessons can be applied to public policy by considering what would happen if, in the name of safety, public officials told captains to never leave port or told aspiring pilots to never leave the ground. The opportunity cost of inaction can be hard to quantify, but it should be clear that if we organized our entire society around a rigid application of the precautionary principle, progress and prosperity would suffer.

Heavy-handed preemptive restraints on creative acts can have deleterious effects because they raise barriers to entry, increase compliance costs, and create more risk and uncertainty for entrepreneurs and investors. Thus, it is the unseen costs—primarily in the form of forgone innovation opportunities—that makes the precautionary principle so problematic as a policy default. This is why scientist Martin Rees speaks of “the hidden cost of saying no” that is associated with the precautionary principle.[6]

The precise way the precautionary principle leads to this result is that it derails the so-called learning curve by limiting opportunities to learn from trial-and-error experimentation with new and better ways of doing things.[7] The learning curve refers to the way that individuals, organizations, or industries are able to learn from their mistakes, improve their designs, enhance productivity, lower costs, and then offer superior products based on the resulting knowledge.[8] In his recent book, Where Is My Flying Car?, J. Storrs Hall documents how, over the last half century, “regulation clobbered the learning curve” for many important technologies in the U.S., especially nuclear, nanotech, and advanced aviation.[9] Hall shows how society was denied many important innovations due to endless foot-dragging or outright opposition to change from special interests, anti-innovation activists, and over-zealous bureaucrats.

In many cases, innovators don’t even know what they are up against because, as many scholars have noted, “the precautionary principle, in all of its forms, is fraught with vagueness and ambiguity.”[10] It creates confusion and fear about the wisdom of taking action in the face of uncertainty. Worst case thinking paralyzes regulators who aim to “play it safe” at all costs. The result is an endless snafu of red tape as layer upon layer of mandates build up and block progress. The result is what many scholars now decry as a culture of “vetocracy,” which describes the many veto points within modern political systems that hold back innovation, development and economic opportunity.[11] This endless accumulation of potential veto points in the policy process in the form of mandates and restrictions can greatly curtail innovation opportunities. “Like sediment in a harbor, law has steadily accumulated, mainly since the 1960s, until most productive activity requires slogging through a legal swamp,” says Philip K. Howard, chair of Common Good.[12] “Too much law,” he argues, “can have similar effects as too little law,” because:

People slow down, they become defensive, they don’t initiate projects because they are surrounded by legal risks and bureaucratic hurdles. They tiptoe through the day looking over their shoulders rather than driving forward on the power of their instincts. Instead of trial and error, they focus on avoiding error.[13]

This is exactly why it is important that policymakers not get too caught up in attempts to preemptively resolve every potential hypothetical worst case scenarios associated with AI technologies. The problem with that approach was succinctly summarized by the political scientist Aaron Wildavsky when he noted, “If you can do nothing without knowing first how it will turn out, you cannot do anything at all.”[14] Or, as I have stated in a book on this topic, “living in constant fear of worst-case scenarios—and premising public policy on them—means that best-case scenarios will never come about.”[15]

This does not mean society should dismiss all concerns about the risks surrounding AI. Some technological risks do necessitate a degree of precautionary policy, but proportionality is crucial, notes Gabrielle Bauer, a Toronto-based medical writer. “Used too liberally,” she argues, “the precautionary principle can keep us stuck in a state of extreme risk-aversion, leading to cumbersome policies that weigh down our lives. To get to the good parts of life, we need to accept some risk.”[16] It is not enough to simply hypothesize that certain AI innovations might entail some risk. The critics need to prove it using risk analysis techniques that properly weigh both the potential costs and benefits.[17] Moreover, when conducting such analyses, the full range of trade-offs associated with preemptive regulation must be evaluated. Again, where precautionary constraints might deny society life-enriching devices or services, those costs must be acknowledged.

Generally speaking, the most extreme precautionary controls should only be imposed when the potential harms in question are highly probable, tangible, immediate, irreversible, catastrophic, or directly threatening to life and limb in some fashion.[18] In the context of AI and ML systems, it may be the case that such a test is satisfied already for law enforcement use of certain algorithmic profiling techniques. And that test is satisfied for so-called “killer robots,” or autonomous military technology.[19] These are often described as “existential risks.” The precautionary principle is the right default in these cases because it is abundantly clear how unrestricted use would have catastrophic consequences. For similar reasons, governments have long imposed comprehensive restrictions on certain types of weapons.[20] And although nuclear and chemical technologies have many important applications, their use must also be limited to some degree even outside of militaristic applications because they can pose grave danger if misused.

But the vast majority of AI-enabled technologies are not like this. Most innovations should not be treated the same a hand grenade or a ticking time bomb. In reality, most algorithmic failures will be more mundane and difficult to foresee in advance. By their very nature, algorithms are constantly evolving because programs and systems are being endlessly tweaked by designers to improve them. In his books on the evolution of engineering and systems design, Henry Petroski has noted that “the shortcomings of things are what drive their evolution.”[21] The normal state of things is “ubiquitous imperfection,” he notes, and it is precisely that reality that drives efforts to continuously innovate and iterate.[22]

Regulations rooted in the precautionary principle hope to preemptively find and address product imperfections before any harm comes from them. In reality, and as explained more below, it is only through ongoing experimentation that we find both the nature of failures and the knowledge to know how to correct them. As Petroski observes, “the history of engineering in general, may be told in its failures as well as in its triumphs. Success may be grand, but disappointment can often teach us more.”[23] This is particularly true for complex algorithmic systems, where rapid-fire innovation and incessant iteration are the norm.

Importantly, the problem with precautionary regulation for AI is not just that it might be over-inclusive in seeking to regulate hypothetical problems that never develop. Precautionary regulation can also be under-inclusive by missing problematic behavior or harms that no one anticipated before the fact. Only experience and experimentation reveal certain problems.

In sum, we should not presume that there is a clear preemptive regulatory solution to every problem some people raise about AI, nor should we presume we can even accurately identify all such problems that might come about in the future. Moreover, some risks will never be eliminated entirely, meaning that risk mitigation is the wiser approach. This is why a more flexible bottom-up governance strategy focused on responsiveness and resiliency makes more sense than heavy-handed, top-down strategies that would only avoid risks by making future innovations extremely difficult if not impossible.

The “Proactionary Principle” is the Better Default for AI Policy

The previous section made it clear why the precautionary principle should generally not be used as our policy default if we hope to encourage the development of AI applications and services. What we need is a policy approach that:

  • objectively evaluates the concerns raised about AI systems and applications;
  • considers whether more flexible governance approaches might be available to address them; and,
  • does so without resorting to the precautionary principle as a first-order response.

The proactionary principle is the better general policy default for AI because it satisfies these three objectives.[24] Philosopher Max More defines the proactionary principle as the idea that policymakers should, “[p]rotect the freedom to innovate and progress while thinking and planning intelligently for collateral effects.”[25] There are different names for this same concept, including the innovation principle, which Daniel Castro and Michael McLaughlin of the Information Technology and Innovation Foundation say represents the belief that “the vast majority of new innovations are beneficial and pose little risk, so government should encourage them.”[26] Permissionless innovation is another name for the same idea. Permissionless innovation refers to the idea that experimentation with new technologies and business models should generally be permitted by default.[27]

What binds these concepts together is the belief that innovation should generally be treated as innocent until proven guilty. There will be risks and failures, of course, but the permissionless innovation mindset views them as important learning experiences. These experiences are chances for individuals, organizations, and all of society to make constant improvements through incessant experimentation with new and better ways of doing things.[28] As Virginia Postrel argued in her 1998 book, The Future and Its Enemies, progress demands “a decentralized, evolutionary process” and mindset in which mistakes are not viewed as permanent disasters but instead as “the correctable by-products of experimentation.”[29] “No one wants to learn by mistakes,” Petroski once noted, “but we cannot learn enough from successes to go beyond the state of the art.”[30] Instead we must realize, as other scholars have observed, that “[s]uccess is the culmination of many failures”[31] and understand “failure as the natural consequence of risk and complexity.”[32]

This is why the default for public policy for AI innovation should, whenever possible, be more green lights than red ones to allow for the maximum amount of trial-and-error experimentation, which encourages ongoing learning.[33] “Experimentation matters,” observes Stefan H. Thomke of the Harvard Business School, “because it fuels the discovery and creation of knowledge and thereby leads to the development and improvement of products, processes, systems, and organizations.”[34]

Obviously, risks and mistakes are “the very things regulators inherently want to avoid,”[35] but “if innovators fear they will be punished for every mistake,” Daniel Castro and Alan McQuinn argue, “then they will be much less assertive in trying to develop the next new thing.”[36] And for all the reasons already stated, that would represent the end of progress because it would foreclose the learning process that allows society to discover new, better, and safer ways of doing things. Technology author Kevin Kelly puts it this way:

technologies must be evaluated in action, by action. We test them in labs, we try them out in prototypes, we use them in pilot programs, we adapt our expectations, we monitor their alterations, we redefine their aims as they are modified, we retest them given actual behavior, we re-direct them to new jobs when we are not happy with their outcomes.[37]

In other words, the proactionary principle appreciates the benefits that flow from learning by doing. The goal is to continuously assess and prioritize risks from natural and human-made systems alike, and then formulate and reformulate our toolkit of possible responses to those risks using the most practical and effective solutions available. This should make it clear that the proactionary approach is not synonymous with anarchy. Various laws, government bodies, and especially the courts play an important role in protecting rights, health, and order. But policies need to be formulated such that innovators and innovation are given the benefit of the doubt and risks are analyzed and addressed in a more flexible fashion.

Some of the most effective ways to address potential AI risks already exist in the form of “soft law” and decentralized governance solution. These will be discussed at greater length below. But existing legal remedies include various common law solutions (torts, class actions, contract law, etc), recall authority possessed by many regulatory agencies, and various consumer protection policies. Ex post remedies are generally superior to ex ante prior restraints if we hope to maximize innovation opportunities. Ex ante regulatory defaults are too often set closer to the red light of the precautionary principle and then enforced through volumes of convoluted red tape.

This is what the World Economic Forum has referred to as a “regulate-and-forget” system of governance,[38] or what others call a “build-and-freeze model” or regulation.[39] In such technological governance regimes, older rules are almost never revisited, even after new social, economic, and technical realities render them obsolete or ineffective.[40] A 2017 survey of U.S. Code of Regulations by Deloitte consultants revealed that 68 percent of federal regulations have never been updated and that 17 percent have only been updated once.[41] Public policies for complex and fast-moving technologies like AI cannot be set in stone and forgotten like that if America hopes to remain on the cutting edge of this sector.

Advocates of the proactionary principle look to counter this problem not by eliminating all laws or agencies, but by bringing them in line with flexible governance principles rooted in more decentralized approaches to policy concerns.[42] As many regulatory advocates suggest, it is important to embed or “bake in” various ethical best practices into AI systems to ensure that they benefit humanity. But this, too, is a process of ongoing learning and there are many ways to accomplish such goals without derailing important technological advances. What is often referred to as “value alignment” or “ethically-aligned design” is challenged by the fact that humans regularly disagree profoundly about many moral issues.[43] “Before we can put our values into machines, we have to figure out how to make our values clear and consistent,” says Harvard University psychologist Joshua D. Greene.[44]

The “Three Laws of Robotics” famously formulated decades ago by Isaac Asimov in his science fiction stories continue to be widely discussed today as a guide to embedding ethics into machines.[45] They read:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

What is usually forgotten about these principles, as AI expert Melanie Mitchell reminds us, is the way Asimov, “often focused on the unintended consequences of programming ethical rules into robots,” and how he made it clear that, if applied too literally, “such a set of rules would inevitably fail.”[46]

This is why flexibility and humility are essential virtues when thinking about AI policy. The optimal governance regime for AI can be shaped by responsible innovation practices and embed important ethical principles by design without immediately defaulting to a rigid application of the precautionary principle.[47] In other words, an innovation policy regime rooted in the proactionary principle can also be infused with the same values that animate a precautionary principle-based system.[48] The difference is that the proactionary principle-based approach will look to achieve these goals in a more flexible fashion using a variety of experimental governance approaches and ex post legal enforcement options, while also encouraging still more innovation to solve problems past innovations may have caused.

To reiterate, not every AI risk is foreseeable, and many risks and harms are more amorphous or uncertain. In this sense, the wisest governance approach for AI was recently outlined by the National Institute of Standards and Technology (NIST) in its initial draft AI Risk Management Framework, which is a multistakeholder effort “to describe how the risks from AI-based systems differ from other domains and to encourage and equip many different stakeholders in AI to address those risks purposefully.”[49] NIST notes that the goal of the Framework is:

to be responsive to new risks as they emerge rather than enumerating all known risks in advance. This flexibility is particularly important where impacts are not easily foreseeable, and applications are evolving rapidly. While AI benefits and some AI risks are well-known, the AI community is only beginning to understand and classify incidents and scenarios that result in harm.[50]

This is a sensible framework for how to address AI risks because it makes it clear that it will be difficult to preemptively identify and address all potential AI risks. At the same time, there will be a continuing need to advance AI innovation while addressing AI-related harms. The key to striking that balance will be decentralized governance approaches and soft law techniques described below.

[Note: The subsequent sections of the study will detail how decentralized governance approaches and soft law techniques already are helping to address concerns about AI risks.]

Endnotes:

[1]     Adam Thierer, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom, 2nd ed. (Arlington, VA: Mercatus Center at George Mason University, 2016): 1-6, 23-38; Adam Thierer, Evasive Entrepreneurs & the Future of Governance (Washington, DC: Cato Institute, 2020): 48-54.

[2]     “Wingspread Statement on the Precautionary Principle,” January 1998, https://www.gdrc.org/u-gov/precaution-3.html.

[3]     Cass R. Sunstein, Laws of Fear: Beyond the Precautionary Principle (Cambridge, UK: Cambridge University Press, 2005). (“The Precautionary Principle takes many forms. But in all of them, the animating idea is that regulators should take steps to protect against potential harms, even if causal chains are unclear and even if we do not know that those harms will come to fruition.”)

[4]     Henk van den Belt, “Debating the Precautionary Principle: ‘Guilty until Proven Innocent’ or ‘Innocent until Proven Guilty’?” Plant Physiology 132 (2003): 1124.

[5]     H.W. Lewis, Technological Risk (New York: WW. Norton & Co., 1990): x. (“The history of the human race would be dreary indeed if none of our forebears had ever been willing to accept risk in return for potential achievement.”)

[6]     Martin Rees, On the Future: Prospects for Humanity (Princeton, NJ: Princeton University Press, 2018): 136.

[7]     Adam Thierer, “Failing Better: What We Learn by Confronting Risk and Uncertainty,” in Sherzod Abdukadirov (ed.), Nudge Theory in Action: Behavioral Design in Policy and Markets (Palgrave Macmillan, 2016): 65-94.

[8]     Adam Thierer, “How to Get the Future We Were Promised,” Discourse, January 18, 2022, https://www.discoursemagazine.com/culture-and-society/2022/01/18/how-to-get-the-future-we-were-promised.

[9]     J. Storrs Hall, Where Is My Flying Car? (San Francisco: Stripe Press, 2021)

[10]    Derek Turner and Lauren Hartzell Nichols, “The Lack of Clarity in the Precautionary Principle,” Environmental Values, Vol 13, No. 4 (2004): 449.

[11]    William Rinehart, “Vetocracy, the Costs of Vetos and Inaction,” Center for Growth & Opportunity at Utah State University, March 24, 2022, https://www.thecgo.org/benchmark/vetocracy-the-costs-of-vetos-and-inaction; Adam Thierer, “Red Tape Reform is the Key to Building Again,” The Hill, April 28, 2022, https://thehill.com/opinion/finance/3470334-red-tape-reform-is-the-key-to-building-again.

[12]    Philip K. Howard, “Radically Simplify Law,” Cato Institute, Cato Online Forum, http://www.cato.org/publications/cato-online-forum/radically-simplify-law.

[13]    Ibid.

[14]    Aaron Wildavsky, Searching for Safety (New Brunswick, NJ: Transaction Publishers, 1989): 38.

[15]    Thierer, Permissionless Innovation, at 2.

[16]    Gabrielle Bauer, “Danger: Caution Ahead,” The New Atlantis, February 4, 2022, https://www.thenewatlantis.com/publications/danger-caution-ahead.

[17]    Richard B. Belzer, “Risk Assessment, Safety Assessment, and the Estimation of Regulatory Benefits” (Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA, 2012), 5, http://mercatus.org/publication/risk-assessment-safety-assessment-and-estimation-regulatory-benefits; John D. Graham and Jonathan Baert Wiener, eds. Risk vs. Risk: Tradeoffs in Protecting Health and the Environment, (Cambridge, MA: Harvard University Press, 1995).

[18]    Thierer, Permissionless Innovation, at 33-8.

[19]    Adam Satariano, Nick Cumming-Bruce and Rick Gladstone, “Killer Robots Aren’t Science Fiction. A Push to Ban Them Is Growing,” New York Times, December 17, 2021, https://www.nytimes.com/2021/12/17/world/robot-drone-ban.html.

[20]    Adam Thierer, “Soft Law: The Reconciliation of Permissionless & Responsible Innovation,” in Adam Thierer, Evasive Entrepreneurs & the Future of Governance (Washington, DC: Cato Institute, 2020): 183-240, https://www.mercatus.org/publications/technology-and-innovation/soft-law-reconciliation-permissionless-responsible-innovation.

[21]    Henry Petroski, The Evolution of Useful Things (New York: Vintage Books, 1994): 34.

[22]    Ibid., 27,

[23]    Henry Petroski, To Engineer is Human: The Role of Failure in Successful Design (New York: Vintage, 1992): 9.

[24]    James Lawson, These Are the Droids You’re Looking For: An Optimistic Vision for Artificial Intelligence, Automation and the Future of Work (London: Adam Smith Institute, 2020): 86, https://www.adamsmith.org/research/these-are-the-droids-youre-looking-for.

[25]    Max More, “The Proactionary Principle (March 2008),” Max More’s Strategic Philosophy, March 28, 2008, http://strategicphilosophy.blogspot.com/2008/03/proactionary-principle-march-2008.html.

[26]    Daniel Castro & Michael McLaughlin, “Ten Ways the Precautionary Principle Undermines Progress in Artificial Intelligence,” Information Technology and Innovation Foundation, February 4, 2019, https://itif.org/publications/2019/02/04/ten-ways-precautionary-principle-undermines-progress-artificial-intelligence.

[27]    Thierer, Permissionless Innovation.

[28]    Thierer, “Failing Better.”

[29]    Virginia Postrel, The Future and Its Enemies (New York: The Free Press, 1998): xiv.

[30]    Henry Petroski, To Engineer is Human: The Role of Failure in Successful Design (New York: Vintage, 1992): 62.

[31]    Kevin Ashton, How to Fly a Horse: The Secret History of Creation, Invention, and Discovery (New York: Doubleday, 2015): 67.

[32]    Megan McArdle, The Up Side of Down: Why Failing Well is the Key to Success (New York: Viking, 2014), 214.

[33]    F. A. Hayek, The Constitution of Liberty (London: Routledge, 1960, 1990): 81. (“Humiliating to human pride as it may be, we must recognize that the advance and even preservation of civilization are dependent upon a maximum of opportunity for accidents to happen.”)

[34]    Stefan H. Thomke, Experimentation Matters: Unlocking the Potential of New Technologies for Innovation (Harvard Business Review Press, 2003), 1.

[35]    Daniel Castro and Alan McQuinn, “How and When Regulators Should Intervene,” Information Technology and Innovation Foundation Reports, (February 2015): 2 http://www.itif.org/publications/how-and-when-regulators-should-intervene.

[36]    Ibid.

[37]    Kevin Kelly, “The Pro-Actionary Principle,” The Technium, November 11, 2008, https://kk.org/thetechnium/the-pro-actiona.

[38]    World Economic Forum, Agile Regulation for the Fourth Industrial Revolution (Geneva: Switzerland: 2020): 4, https://www.weforum.org/projects/agile-regulation-for-the-fourth-industrial-revolution.

[39]    Jordan Reimschisel and Adam Thierer, “’Build & Freeze’ Regulation Versus Iterative Innovation,” Plain Text, November 1, 2017, https://readplaintext.com/build-freeze-regulation-versus-iterative-innovation-8d5a8802e5da.

[40]    Adam Thierer, “Spring Cleaning for the Regulatory State,” AIER, May 23, 2019, https://www.aier.org/article/spring-cleaning-for-the-regulatory-state.

[41]    Daniel Byler, Beth Flores & Jason Lewris, “Using Advanced Analytics to Drive Regulatory Reform: Understanding Presidential Orders on Regulation Reform,” Deloitte, 2017, https://www2.deloitte.com/us/en/pages/public-sector/articles/advanced-analytics-federal-regulatory-reform.html.

[42]    Adam Thierer, Governing Emerging Technology in an Age of Policy Fragmentation and Disequilibrium, American Enterprise Institute (April 2022), https://platforms.aei.org/can-the-knowledge-gap-between-regulators-and-innovators-be-narrowed.

[43]    Brian Christian, The Alignment Problem: Machine Learning and Human Values (New York: W.W. Norton & Company, 2020).

[44]    Joshua D. Greene, “Our Driverless Dilemma,” Science (June 2016): 1515.

[45]    Susan Leigh Anderson, “Asimov’s ‘Three Laws of Robotics’ and Machine Metaethics,” AI and Society, Vol. 22, No. 4, (2008): 477-493.

[46]    Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (New York: Farrar, Straus and Giroux, 2019): 126 [Kindle edition.]

[47]    Thomas A. Hemphill, “The Innovation Governance Dilemma: Alternatives to the Precautionary Principle,” Technology in Society, Vol. 63 (2020): 6, https://ideas.repec.org/a/eee/teinso/v63y2020ics0160791x2030751x.html.

[48]    Adam Thierer, “Are ‘Permissionless Innovation’ and ‘Responsible Innovation’ Compatible?” Technology Liberation Front, July 12, 2017, https://techliberation.com/2017/07/12/are-permissionless-innovation-and-responsible-innovation-compatible.

[49]    The National Institute of Standards and Technology, “AI Risk Management Framework: Initial Draft,” (March 17, 2022): 1, https://www.nist.gov/itl/ai-risk-management-framework.

[50]    Ibid., at 5.

]]> https://techliberation.com/2022/05/26/the-proper-governance-default-for-ai/feed/ 4 76994 New Report: “Governing Emerging Technology in an Age of Policy Fragmentation and Disequilibrium” https://techliberation.com/2022/05/02/new-report-governing-emerging-technology-in-an-age-of-policy-fragmentation-and-disequilibrium/ https://techliberation.com/2022/05/02/new-report-governing-emerging-technology-in-an-age-of-policy-fragmentation-and-disequilibrium/#respond Mon, 02 May 2022 18:00:35 +0000 https://techliberation.com/?p=76982

The American Enterprise Institute (AEI) has kicked off a new project called “Digital Platforms and American Life,” which will bring together a variety of scholars to answer the question: How should policymakers think about the digital platforms that have become embedded in our social and civic life? The series, which is being edited by AEI Senior Fellow Adam J. White, highlights how the democratization of knowledge and influence in the Internet age comes with incredible opportunities but also immense challenges. The contributors to this series will approach these issues from various perspectives and also address different aspects of policy as it pertains to the future of technological governance.

It is my honor to have the lead paper in this new series. My 19-page essay is entitled, Governing Emerging Technology in an Age of Policy Fragmentation and Disequilibrium, and it represents my effort to concisely tie together all my writing over the past 30 years on governance trends for the Internet and related technologies. The key takeaways from my essay are:

  • Traditional governance mechanisms are being strained by modern technological and political realities. Newer technologies, especially digital ones, are developing at an ever-faster rate and building on top of each other, blurring lines between sectors.
  • Congress has failed to keep up with the quickening pace of technological change. It also continues to delegate most of its constitutional authority to agencies to deal with most policy concerns. But agencies are overwhelmed too. This situation is unlikely to change, creating a governance gap.
  • Decentralized governance techniques are filling the gap. Soft law—informal, iterative, experimental, and collaborative solutions—represents the new normal for technological governance. This is particularly true for information sectors, including social media platforms, for which the First Amendment acts as a major constraint on formal regulation anyway.
  • No one-size-fits-all tool can address the many governance issues related to fast-paced science and technology developments; therefore, decentralized governance mechanisms may be better suited to address newer policy concerns.

My arguments will frustrate many people of varying political dispositions because I adopt a highly pragmatic approach to technological governance. No matter what your preferred ideal state of affairs looks like in terms of technological governance, you’re bound to be disappointed by the way high-tech policy is unfolding today. Many people desire bright-letter hard law that has government(s) establishing comprehensive, precautionary regulation of various tech sectors. Others prefer a clearly defined but more light-touch policy regime for emerging technology. Alas, neither of these preferred hard law dispositions describe the world we live in today, nor will either of them likely govern the future. My essay outlines a variety of reasons why such hard law approaches are breaking down today, including general legislative dysfunctionalism, the endless delegation of power from Congress to regulatory agencies or the states, and the the intensifying “pacing problem” (i.e., the fact that technological change is happening at a must faster rate than policy change).

In light of this, I argue:

it is smart to think practically about alternative governance frameworks when traditional hard-law approaches prove slow or ineffective in addressing governance needs. It is also wise to consider alternative governance frameworks that might address the occasional downsides of disruptive technologies without completely foreclosing ongoing innovation opportunities the way many hard-law solutions would.

I also show that, whether anyone cares to admit it or not, we already live in a world of multiplying “soft law” mechanisms and decentralized governance approaches. I use the example of how these new governance trends are unfolding for autonomous vehicles, but note how we see decentralized governance approaches being utilized in many other sectors. This is equally true across the Atlantic where the United Kingdom is increasingly experimenting with new governance approached for emerging technologies.

What counts as “soft law” or “decentralized governance” is an open-ended and ever-changing topic of discussion. But I note that it, at a minimum, it includes: multi-stakeholder processes, experimental “sandboxes,” industry best practices or codes of conduct, technical standards, private certifications, agency workshops and guidance documents, informal negotiations, and education and awareness building efforts. I unpack these ideas in the essay in more detail.

For social media, soft law approaches are the current governance norm, even as hard law regulatory proposals continue to multiply rapidly. But I note that despite all that pressure for more formal regulatory governance of social media platforms, the First Amendment presents a formidable barrier to most of those proposals. Thus, soft law will continue to be the dominant governance approach here. I also conclude by predicting that that soft law will become the dominant approach for artificial intelligence, too, even as regulatory proposals multiply there as well.

I’ll have more to say about my paper and other papers in the AEI series in coming weeks and month. For now, I encourage you to jump over to the website AEI has set up for the series and take a look at my new paper.

Additional Reading :

]]> https://techliberation.com/2022/05/02/new-report-governing-emerging-technology-in-an-age-of-policy-fragmentation-and-disequilibrium/feed/ 0 76982 New Jurimetrics Article: “Soft Law in U.S. ICT Sectors: Four Case Studies” https://techliberation.com/2021/02/01/new-jurimetrics-article-soft-law-in-u-s-ict-sectors-four-case-studies/ https://techliberation.com/2021/02/01/new-jurimetrics-article-soft-law-in-u-s-ict-sectors-four-case-studies/#comments Mon, 01 Feb 2021 21:02:45 +0000 https://techliberation.com/?p=76836

After a slight delay, Jurimetrics has finally published my latest law review article, “Soft Law in U.S. ICT Sectors: Four Case Studies.” It is part of a major symposium that Arizona State University (ASU) Law School put together on “Governing Emerging Technologies Through Soft Law: Lessons For Artificial Intelligence” for the journal. I was 1 of 4 scholars invited to pen foundational essays for this symposium. Jurimetrics is a official publication of the American Bar Association’s Section of Science & Technology Law.

This report was a major undertaking that involved dozens of interviews, extensive historic research, several events and presentations, and then numerous revisions before the final product was released. The final PDF version of the journal article is attached.

Here is the abstract:

Traditional hard law tools and processes are struggling to keep up with the rapid pace of innovation in many emerging technologies sectors. As a result, policy­makers in the United States rely increasingly on less formal “soft law” governance mech­anisms to address concerns surrounding many newer technologies. This Article explores four case studies from different information technology areas where soft law mechanisms have already been utilized to address governance concerns. These four sectoral case stud­ies include domain name management, content oversight, privacy policy, and cyberse­curity matters. After considering the various soft law mechanisms used to address those issues, the Article concludes with some general thoughts about the effectiveness of those approaches and what lessons those case studies might hold for the use of soft law in other emerging technology sectors and contexts.

]]> https://techliberation.com/2021/02/01/new-jurimetrics-article-soft-law-in-u-s-ict-sectors-four-case-studies/feed/ 6 76836 Latest Soft Law Development: DoT’s NETT Council Report https://techliberation.com/2020/07/31/latest-soft-law-development-dots-nett-council-report/ https://techliberation.com/2020/07/31/latest-soft-law-development-dots-nett-council-report/#comments Fri, 31 Jul 2020 18:13:26 +0000 https://techliberation.com/?p=76780

Cover of the Pathways DocumentOn July 23rd, the U.S. Department of Transportation (DoT) released Pathways to the Future of Transportation, which was billed as “a policy document that is intended to serve as a roadmap for innovators of new cross modal technologies to engage with the Department.” This guidance document was created by a new body called the Non-Traditional and Emerging Transportation Technology (NETT) Council, which was formed by U.S. Transportation Secretary Elaine L. Chao last year. The NETT Council is described as “an internal deliberative body to identify and resolve jurisdictional and regulatory gaps that may impede the deployment of new technologies.”

The creation of NETT Council and the issuance of its first major report highlight the continued growth of “soft law” as a major governance trend for emerging technology in the US. Soft law refers to informal, collaborative, and constantly evolving governance mechanisms that differ from hard law in that they lack the same degree of enforceability. A partial inventory of soft law methods includes: multistakeholder processes, industry best practices or codes of conduct, technical standards, private certifications, agency workshops and guidance documents, informal negotiations, and education and awareness efforts. But this list of soft law mechanisms is amorphous and ever-changing.

Soft law systems and processes are multiplying at every level of government today: federal, state, local, and even globally. Such mechanisms are being tapped by government bodies today to deal with fast-moving technologies that are evolving faster than the law’s ability to keep up.

The US Department of Transportation has become a leading candidate for Soft Law Central at the federal level. The agency has been tapping a variety of soft law mechanisms and approaches to deal with driverless cars and drone policy issues in particular. (See the essays listed down below for more details).

The NETT Council represents the next wave of this governance trend. We might consider it an effort to bring a greater degree of formality and coordination to the agency’s soft law efforts. The DoT’s overview of the NETT Council explains its purpose as follows:

Inventors and investors approach USDOT to obtain necessary safety authorizations, permits, and funding and often face uncertainty about how to coordinate with the Department. The NETT Council will address these challenges by ensuring that the traditional modal silos at DOT do not impede the safe deployment of new technology. Furthermore, it will give project sponsors a single point of access to discuss plans and proposals.

In its new guidance document, the NETT Council seeks to outline how it will work to develop “the principles informing the [DoT] policies in transformative technologies,” as well as “the overarching regulatory framework for non-traditional and emerging transportation technologies.” A lot of stress is placed on “how the Council will engage with innovators and entrepreneurs” to strike the balance between continued safety and increased innovation.

Although much of the document simply discusses existing agency regulatory authority, the Council also identifies how the agency and its subdivisions will seek a more flexible governance approach going forward. A premium is placed on expanding dialogue among affected parties. The section discussing environmental review requirements is indicative of this, noting: “The Department encourages innovators, project sponsors or proponents to engage in a dialogue with the NETT Council when the proponent anticipates seeking Federal financial assistance or an authorization.”

“Any innovator can approach the NETT Council with its ideas,” the document says in another section, although engagement level may vary by issue and department. It continues on to note that, “during the formation stage, the NETT Council would likely be willing to have an informational meeting and establish a point of contact to maintain a level of awareness for Department staff regarding the new project.” “Successful collaboration tends to be characterized by industry initiation and leadership with a limited and defined federal role,” it notes. Several examples are highlighted.

In addition to the importance of early dialogue between innovators and regulators, the document stresses the dangers associated with regulatory uncertainty. It also includes some discussion about the problems associated with a lack of regulatory flexibility in some instances “and the potential deterrent to innovation caused by attempting to ‘shoehorn’ a particular technology into a regulatory regime that does not fit.” There is also some discussion of how international or private sector standards might help provide governance solutions in some instances.

Again, these are all examples of soft law mechanisms. To be clear, the NETT Council is not proposing the abandonment of hard law enforcement efforts. To the contrary, the document repeatedly reiterates what those powers are and how they might be used. But it is equally clear that the DoT realizes that the old regulatory systems are being severely strained by the “pacing problem,” or the notion that technological developments are often moving considerably faster than traditional regulatory processes.

The NETT Council report is a welcome effort to broaden the dialogue about what sort of governance systems might make the more sense going forward for emerging technologies. This is a pressing problem for the DoT because of the convergence of digital and analog sectors and technologies. AI and machine-learning technologies are invading the crusty old world of transportation networks and regulations. Momentous changes are happening. Law will need to adapt. Soft law systems will increasingly be tapped to help out if for no other reason than there isn’t a better backup plan. If America hopes to be a leader in transportation innovation, new governance approaches will be essential.

Below you will find some additional essays on the growing soft law-ization of technological governance in the US. Many of them are about transportation technologies and recent developments at the federal and state levels. I also recommend this new essay by John Villasenor over at Brookings on “Soft law as a complement to AI regulation.” Finally, if you want to do a deep dive in the nature of soft law and the full range of governance issues associated with it, then you absolutely must follow the work being done by Gary Marchant and his impressive team of colleagues at Arizona State University. Begin with this essay on “Soft Law Governance Of Artificial Intelligence,” and then get your hands on this huge book on the topic that Marchant co-edited. It’s the best thing I have read on soft law and alternative governance systems for emerging technologies.

In the meantime, give the new DoT NETT Council report a glance because, for better or worse, this is what the future of technological governance looks like.

]]> https://techliberation.com/2020/07/31/latest-soft-law-development-dots-nett-council-report/feed/ 2 76780 Can Biohacking & DIY Citizen Science Help Find a COVID Vaccine? https://techliberation.com/2020/07/29/can-biohacking-diy-citizen-science-help-find-a-covid-vaccine/ https://techliberation.com/2020/07/29/can-biohacking-diy-citizen-science-help-find-a-covid-vaccine/#comments Wed, 29 Jul 2020 19:14:24 +0000 https://techliberation.com/?p=76782

In an amazing new MIT Technology Review piece, Antonio Regalado discusses how, “Some scientists are taking a DIY coronavirus vaccine, and nobody knows if it’s legal or if it works.” It is another powerful example of how “citizen-science” and medical self-experimentation (or “biohacking”) is increasingly being used to improve health outcomes, enhance human capabilities, or fight against deadly diseases like COVID. Regalado reports that:

Nearly 200 covid-19 vaccines are in development and some three dozen are at various stages of human testing. But in what appears to be the first “citizen science” vaccine initiative, Estep and at least 20 other researchers, technologists, or science enthusiasts, many connected to Harvard University and MIT, have volunteered as lab rats for a do-it-yourself inoculation against the coronavirus. They say it’s their only chance to become immune without waiting a year or more for a vaccine to be formally approved. Among those who’ve taken the DIY vaccine is George Church, the celebrity geneticist at Harvard University, who took two doses a week apart earlier this month. The doses were dropped in his mailbox and he mixed the ingredients himself.

Regalado notes that this is all happening despite legal and ethical questions:

By distributing directions and even supplies for a vaccine, though, the Radvac group is operating in a legal gray area. The US Food and Drug Administration (FDA) requires authorization to test novel drugs in the form of an investigational new drug approval. But the Radvac group did not ask the agency’s permission, nor did it get any ethics board to sign off on the plan.

Chapter 2 of my latest book (Evasive Entrepreneurs and the Future of Governance) features a discussion of DIY health efforts, citizen-science and biohacking. Average citizens are using new technological capabilities to address health needs, often beyond the confines of the law. Here’s the beginning of that discussion, which starts on p. 79 of the manuscript:

DIY health services and medical devices are on the rise thanks to the combined power of open-source software, 3D printers, cloud computing, and digital platforms that allow information sharing between individuals with specific health needs. Average citizens are using these new technologies to modify their bodies and abilities, often beyond the confines of the law. Welcome to the occasionally scary but oftentimes awe-inspiring world of biohacking. Biohackers are essentially “prosumers,” the term many used a decade ago to describe the way average citizens were taking advantage of new communications and computing technologies to become both producers and consumers of news, information, and entertainment. Pro-sumers evaded traditional industry norms and government regulations that had previously made it difficult for citizens to communicate freely. The same phenomenon is now shaking up the world of health and medicine as pro-sumers use new technological capabilities to take their health into their own hands and likely evade many traditional norms and regulations when doing so.

In other words, we can’t just put the genie back in the bottle with sweeping, repressive regulatory controls. Here’s an essay that Jordan Reimschisel and I wrote last year on “Biohacking, Democratized Medicine, and Health Policy” highlighting the many thorny policy issues in play here, as well as possible governance responses.

In another essay, Jordan and I argued that one of the most important and constructive policy responses would be stepped-up risk education and health literacy initiatives. We need constructive approaches to citizen-science and biohacking to make sure we address serious risks but simultaneously avoid blocking beneficial forms of health and medical innovation that our country desperately needs, especially at this time.

]]> https://techliberation.com/2020/07/29/can-biohacking-diy-citizen-science-help-find-a-covid-vaccine/feed/ 3 76782 Video: My Conversation with the Institute for Economic Inquiry https://techliberation.com/2020/05/31/video-my-conversation-with-the-institute-for-economic-inquiry/ https://techliberation.com/2020/05/31/video-my-conversation-with-the-institute-for-economic-inquiry/#respond Sun, 31 May 2020 13:34:43 +0000 https://techliberation.com/?p=76744

Here’s a webinar video of a discussion I had recently with Kevin Gomez and his colleague at the Institute for Economic Inquiry at Creighton University’s School of Business.  We discussed my new book, Evasive Entrepreneurs and the Future of Governance: How Innovation Improves Economies and Governments and the future of “permissionless innovation” more generally. My thanks to Kevin and his team at Creighton for inviting me to join them for a fun discussion. Topics include:

  • why evasive entrepreneurialism is expanding
  • the growth of innovation arbitrage
  • the difference between technologies that are “born free” versus “born in captivity”
  • the nature of “the pacing problem” and what it means for policy
  • the problem with “set-it-and-forget-it” & “build-and-freeze” regulations
  • technological risk and the potential for “soft law” governance
  • sensible legislative reforms to advance permissionless innovation (such as “the innovator’s presumption” and “the sunsetting imperative”)
  • how the COVID crisis potentially opens the Overton Window to much-needed policy change
]]> https://techliberation.com/2020/05/31/video-my-conversation-with-the-institute-for-economic-inquiry/feed/ 0 76744 Evasive Entrepreneurialism and Technological Civil Disobedience in the Midst of a Pandemic https://techliberation.com/2020/04/28/evasive-entrepreneurialism-and-technological-civil-disobedience-in-the-midst-of-a-pandemic/ https://techliberation.com/2020/04/28/evasive-entrepreneurialism-and-technological-civil-disobedience-in-the-midst-of-a-pandemic/#comments Tue, 28 Apr 2020 22:39:23 +0000 https://techliberation.com/?p=76704

[Originally published on the Cato Institute blog.]

A pandemic is no time for bad governance. As the COVID-19 crisis intensified, bureaucrats and elected officials slumbered. Government regulations prevented many in the private sector from helping with response efforts. The result was a sudden surge of evasive entrepreneurialism and technological civil disobedience. With institutions and policies collapsing around them, many people took advantage of cutting‐​edge technological capabilities to evade public policies that were preventing practical solutions from emerging.

Examples were everywhere. Distilleries started producing hand sanitizers to address shortages while average folks began sharing do‐​it‐​yourself sanitizer recipes online. The Food and Drug Administration (FDA) looked to modify hand sanitizer guidelines quickly to allow for it, but few really cared because those rules weren’t going to stop them. Gray markets in face masks, medical face shields, and respirators developed. Some people and organizations worked together to make medical devices using off‐​the‐​shelf hardware and open source software. More simply, others just fired up sewing machines to make masks—and then, faced with an emerging public health consensus, the guidance from the federal government shifted dramatically: where formerly ordinary people were instructed not to buy or use masks, within a matter of days, the policy reversed, and all were encouraged to make and use cloth protective masks.

Meanwhile, doctors and nurses started “writing the playbook for treating coronavirus patients on the fly” by improvising treatments and then sharing them on social media. A few doctors even converted breathing machines to ventilators themselves using 3-D printed parts to address shortages for their patients even though the FDA had not yet authorized it.

Social media sites were also suddenly filled with discussions about how average people might come together to build tools or share information to assist with virus testing or treatments. A 17‐​year‐​old used his coding skills to build one of the most popular coronavirus‐​tracking websites in the world (ncov2019.live) after noticing how hard it was to use government sites. And two high school science teachers in Tennessee set up testing operations in their school lab to help reduce testing time in their area.

Meanwhile, journalists and columnists like the  Wall Street Journal’s Andy Kessler cheered on such activity, encouraging the public to “innovate from your couch.” Modern digital technologies and platforms that had been pariahs and the target of a regulatory‐​minded “techlash” just a few months earlier suddenly became essential public services that were showered with praise for helping people cope with social distancing and the solitude associated with shelter‐​in‐​place requirements. Headlines in major media outlets explained how “Facebook Is More Trustworthy than the President” and “Twitter Is Making the Coronavirus World a Better Place.”

Philanthropists like Bill Gates were also funding their own solutions. The former Microsoft founder and CEO pointed out that, in an effort to find testing solutions and vaccines, private groups like his Gates Foundation could likely mobilize faster than governments. Gates likely had grown frustrated with government responses after a Seattle‐​based lab that the Gates Foundation funded figured out an effective way to test for coronavirus, only to be blocked from expanding it by over‐​cautious federal bureaucrats. Frustrated by federal intransigence, that Seattle lab started testing for COVID-19 anyway to prove they indeed had an effective test. Commenting on the case study, the New York Times  expressed exasperation about “how existing regulations and red tape—sometimes designed to protect privacy and health—have impeded the rapid rollout of testing nationally.”

Wait, Isn’t All This Illegal?

What is interesting about all these examples of bottom‐​up innovation and evasive entrepreneurialism is that they are remarkably inspiring, but also mostly illegal. Almost all these activities butted up against longstanding regulations governing medical devices, practices, or therapies. Some of those rules are enforced by large and powerful federal bureaucracies like the FDA and Centers for Disease Control and Prevention (CDC).

Others take the form of state‐​based occupational licensing limitations or certificate‐​of‐​need laws, which require healthcare providers to first obtain permission before they open or expand their facilities or services. This crazy quilt of medical laws and regulations accumulated steadily over time, creating what constitutional scholar Timothy Sandefur calls a “permission society,” which values proceduralism and conformity over practicality and common sense.

Eventually, however, the mountains of red tape that the permission society is built upon start to collapse under their own weight. Laws and agencies that previously commanded obedience are now viewed as an opaque, ossified, and confusing morass of one‐​size‐​fits‐​all mandates, prohibitions, and penalties that actually undermine the very health goals they were put in place to achieve. Suddenly, headlines in every major newspaper screamed of how, as it pertained to virus testing procedures, “The Government Failed” (Wall Street Journal) because of “Flawed Tests, Red Tape and Resistance” (Washington Post) and this resulted in “The Lost Month” (New York Times) in the United States.

Eventually, people take notice of how regulators and their rules encumber entrepreneurial activities, and they act to evade them when public welfare is undermined. Working around the system becomes inevitable when the permission society becomes so completely dysfunctional and counterproductive.

Technological Empowerment vs. the Status Quo

What’s going on here, and what lessons can we derive from it?

In a new Cato Institute book, Evasive Entrepreneurs and the Future of Governance, I document how the sort of behavior we have recently witnessed was growing rapidly even before the COVID-19 crisis. In many different contexts, evasive entrepreneurs—innovators who don’t always conform to social or legal norms—are using new technological capabilities to circumvent traditional regulatory systems. They at least want to put pressure on public policymakers to reform or selectively enforce laws and regulations that are outmoded, inefficient, or counterproductive.

Evasive entrepreneurs rely on a strategy of permissionless innovation in both the business world and the political arena. They push back against the permission society by creating exciting new products and services without always receiving the blessing of public officials before doing so. While evasive entrepreneurialism has always been with us to some extent, many of the responses to the pandemic would not have been possible even just a few decades ago. Recent advancements have supercharged in a more technologically empowered world of information abundance and decentralized, inexpensive tools.

As I show in the book, evasive entrepreneurs are taking advantage of the growth of what we might think of as technologies of freedom or resistance. These are devices and platforms that let citizens circumvent (or perhaps just ignore) public policies that limit their liberty or freedom to innovate or to enjoy the fruits of innovation. These can include common tools like smartphones, computers, and various new interactive platforms, as well as more specialized technologies like cryptocurrencies, private drones, immersive technologies (like virtual reality), 3D printers, the “Internet of Things,” and sharing economy platforms and services. But that list just scratches the surface. When the public uses tools such as these to explicitly evade public policies on moral grounds because they find then offensive, illogical, or perhaps just annoying, we can think of that as technological civil disobedience.

Common Sense Prevails

Evasive entrepreneurialism and technological civil disobedience accelerated during the pandemic because both the practicality and morality of government policies came into question in stark fashion. The first month of the crisis witnessed “a torrent of governmental incompetence that is breathtaking in scale,” my Mercatus colleague Scott Sumner argues. “There are regulations so bizarre that if put in a novel no one would believe them,” he notes. “In contrast, the private sector has reacted fairly well, and has been far ahead of the government in most areas.”

Indeed, the pandemic has been a stress test for our institutions, and many of them have failed it. Confusing rules and inflexible agencies that should have been reformed years ago were suddenly exposed and judged harshly. Philip K. Howard, founder of Common Good, says that “Covid‐​19 is the canary in the bureaucratic mine.” Bloated bureaucracies and overbearing regulatory systems, he argues, have created a “toxic atmosphere that silenced common sense” and managed to “institutionalize failure.” Cato’s Paul Matzko has documented how the FDA has been particularly guilty of blocking sensible forms of progress on simple things like face mask production or distribution.

While countless others lambasted the practical failures of our institutions, the morality of government policies was also coming into focus. Why should citizens have their innovative efforts to help others stifled at seemingly every juncture? Must we really follow the law when it undercuts the basic human need to care for others and ourselves?

These are the issues addressed in my new book, which explains the practical reasons why evasive entrepreneurialism is on the rise and then provides a moral defense of it. When innovators and average citizens use tools and technological capabilities to pursue a living, enjoy new experiences, or improve the human condition, they often disrupt legal or social norms in the process. That is not necessarily a bad thing. In fact, evasive entrepreneurialism can transform our society for the better because it can help expand the range of life‐​enriching (and often life‐​saving) innovations. Evasive entrepreneurialism can help citizens pursue lives of their own choosing—both as creators looking for the freedom to earn a living and as individuals looking to discover and enjoy important new goods and services.

Defending evasive entrepreneurialism is easy  after it occurs, but few defend it before or as it is happening. I argue in the book that the freedom to innovate is essential to human betterment—for each of us individually and for civilization as a whole—and that freedom deserves to be taken more seriously today. The COVID-19 pandemic has made this more apparent than ever before.

There are few things more human than acts of invention. At its root, innovation involves efforts to discover new and better ways of solving practical human needs and wants. People have a right to innovate and create technologies because they possess a more general right to take steps to improve their lot in life and the lives of others around them. When misguided or archaic government programs and policies blocked that potential during the pandemic, people began ignoring or evading them. That was both practically sensible and morally justifiable.

Innovation as the New Checks and Balances

By extension, the response to the pandemic has proven the second thesis set forth in my book: Evasive entrepreneurialism and technologically enabled civil disobedience can actually help us improve government by keeping public policies fresh, sensible, and in line with common sense and the consent of the governed. Evasiveness and technological disruption can act as a sort of relief valve or circuit breaker to counteract negative pressures in the system before things break down completely. By challenging legislators and regulators to reevaluate the wisdom of their policies, evasive entrepreneurs can help us break political logjams and force governments to become more adaptive and accountable.

The proof is in the pudding. As the crisis unfolded, agencies at the federal, state, and local levels were forced to suspend hundreds of regulations that were clearly undermining helpful responses. These “rule departures” would not have been necessary if governments had engaged in periodic spring cleanings earlier. When COVID-19 hit, it became essential to suspend or repeal hundreds of misguided old rules that clearly undermined public health. The only question now is whether those inefficient, counterproductive policies will be put back on the books to do harm again in the next crisis.

But even before the current crisis, rule departures by government actors were becoming more common because  even government officials could no longer understand their own rules. Just as private citizens have increasingly resorted to evasive techniques to get things done, many regulatory agencies have given up trying to “go by the book” themselves because endless regulatory accumulation has made it impossible to understand what the law means.

My book documents many cases of public officials essentially ignoring their own policies and making up governance solutions as they go along. This is another sign of profound institutional failure, yet it should also give us some hope that even policymakers themselves now realize that government cannot just grow forever without breaking down at some point. The need for comprehensive reform is now abundantly clear, and the pandemic has moved the so‐​called “Overton Window” (i.e., the acceptable range of possible policy reforms) on many fronts.

A New Approach to Governance

Policymakers need a new approach for technological governance that is more in line with modern realities. Flexibility and humility will be essential. Regulators do not need to throw out the old rulebooks altogether, though. Some precautionary rules still make sense, particularly in cases involving extreme risk. But why not embrace the entrepreneurial spirit of the citizenry and allow more experimental trials, flexible testing procedures, and perhaps even prizes for particularly innovative ideas?

When enforcing the rules that remain on the books, policymakers should also consider targeted waivers and ex post regulatory reviews as opposed to ex ante regulatory prohibitions on any and all evasive innovations. Liability rules can also be tweaked so innovators do not have to live in constant fear of getting sued for trying to make the world a better place. Finally, post‐​market monitoring and recall notices can also be used to ensure flexible experiments have some regulatory guardrails.

But shutting down creative solutions and unique thinking simply because they run counter to some crusty old rulebook is never the right response. We should view evasive entrepreneurialism as an important part of a broader discovery process that incorporates the profound importance of ongoing, decentralized, trial‐​and‐​error experimentation to the process of societal learning and improvement. Lawmakers should find a way to accommodate a little more outside‐​the‐​box thinking and innovating—and not just when our lives are on the line.

Additional Reading 

]]> https://techliberation.com/2020/04/28/evasive-entrepreneurialism-and-technological-civil-disobedience-in-the-midst-of-a-pandemic/feed/ 1 76704 “Evasive Entrepreneurs” – 13 Key Terms from the Book https://techliberation.com/2020/04/28/evasive-entrepreneurs-13-key-terms-from-the-book/ https://techliberation.com/2020/04/28/evasive-entrepreneurs-13-key-terms-from-the-book/#comments Tue, 28 Apr 2020 13:09:58 +0000 https://techliberation.com/?p=76701

My latest book, Evasive Entrepreneurs and the Future of Governance How Innovation Improves Economies and Governments, is now live. Here’s the launch essay and online launch event. Also, here’s a summary of 10 major arguments advanced in the book. I will have more to say about the book in coming weeks, but here is a list of 13 key terms discussed in the text. This list appears at the end of the introduction to the book:

  1. Compliance paradox: The situation in which heightened legal or regulatory efforts fail to reverse unwanted behavior and instead lead to increased legal evasion and additional enforcement problems.
  2. Demosclerosis: Growing government dysfunction brought on by the inability of public institutions to adapt to change, especially technological change.
  3. Evasive entrepreneurs: Innovators who do not always conform to social or legal norms.
  4. Free innovation: Bottom-up, noncommercial forms of innovation that often take on an evasive character. Free innovation is sometimes called “grassroots” or “household” innovation or “social entrepreneurialism.” Even though it is typically noncommercial in character, free innovation often involves regulatory entrepreneurialism and technological civil disobedience.
  5. Innovation arbitrage: The movement of ideas, innovations, or operations to jurisdictions that provide legal and regulatory environments most hospitable to entrepreneurial activity. It can also be thought of as a form of jurisdictional shopping and can be facilitated by competitive federalism.
  6. Innovation culture: The various social and political attitudes and pronouncements toward innovation, technology, and entrepreneurial activities that, taken together, influence the innovative capacity of a culture or nation.
  7. Pacing problem: A term that generally refers to the inability of legal or regulatory regimes to keep up with the intensifying pace of technological change.
  8. Permissionless innovation: The general notion that “it’s easier to ask forgiveness than it is to get permission.” As a policy vision, it refers to the idea that experimentation with new technologies and innovations should generally be permitted by default.
  9. Precautionary principle: The practice of crafting public policies to control or limit innovations until their creators can prove that they will not cause any harm or disruptions.
  10. Regulatory entrepreneurs: Evasive entrepreneurs who set out to intentionally challenge and change the law through their innovative activities. In essence, policy change is part of their business model.
  11. Soft law: Informal, collaborative, and constantly evolving governance mechanisms that differ from hard law in that they lack the same degree of enforceability.
  12. Technological civil disobedience: The technologically enabled refusal of individuals, groups, or businesses to obey certain laws or regulations because they find them offensive, confusing, time-consuming, expensive, or perhaps just annoying and irrelevant.
  13. Technologies of freedom: Devices and platforms that let citizens openly defy (or perhaps just ignore) public policies that limit their liberty or freedom to innovate. Another term with the same meaning is “technologies of resistance.”
]]> https://techliberation.com/2020/04/28/evasive-entrepreneurs-13-key-terms-from-the-book/feed/ 2 76701 “Evasive Entrepreneurs” – 10 Highlights from the Book https://techliberation.com/2020/04/28/evasive-entrepreneurs-10-highlights-from-the-book/ https://techliberation.com/2020/04/28/evasive-entrepreneurs-10-highlights-from-the-book/#comments Tue, 28 Apr 2020 13:08:40 +0000 https://techliberation.com/?p=76698

I’m pleased to announce that the Cato Institute has just published my latest book, Evasive Entrepreneurs and the Future of Governance How Innovation Improves Economies and Governments. Here’s my introductory launch essay about the book as well as the online launch event. And here’s a list of 13 key terms used throughout the book.

In coming days and weeks I will be occasionally blogging about different arguments made in the 368-page book, but here’s a quick summary of some of the key points I make in the book. These ten passages are pulled directly from the text:

  1. “the freedom to innovate is essential to human betterment for each of us individually and for civilization as a whole. That freedom deserves to be taken more seriously today.”
  2. “Entrepreneurialism and technological innovation are the fundamental drivers of economic growth and of the incredible advances in the everyday quality of life we have enjoyed over time. They are the key to expanding economic opportunities, choice, and mobility.”
  3. “Unfortunately, many barriers exist to expanding innovation opportunities and our entrepreneurial efforts to help ourselves, our loved ones, and others. Those barriers include occupational licensing rules, cronyism-based industrial protectionist schemes, inefficient tax schemes, and many other layers of regulatory red tape at the federal, state, and local levels. We should not be surprised, therefore, when citizens take advantage of new technological capabilities to evade some of those barriers in pursuit of their right to earn a living, to tinker with or try doing new things, or just to learn about the world and serve it better.”
  4. “Evasive entrepreneurs rely on a strategy of permissionless innovation in both the business world and the political arena. They push back against ‘the Permission Society,’ or the convoluted labyrinth of permits and red tape that often encumber entrepreneurial activities.” 
  5. “We should be willing to tolerate a certain amount of such outside-the-box thinking because entrepreneurialism expands opportunities for human betterment by constantly replenishing the well of important, life-enhancing ideas and applications.”
  6. “we should better appreciate how creative acts and the innovations they give rise to can help us improve government by keeping public policies fresh, sensible, and in line with common sense and the consent of the governed.”
  7. “Evasive entrepreneurialism is not so much about evading law altogether as it is about trying to get interesting things done, demonstrating a social or an economic need for new innovations in the process, and then creating positive leverage for better results when politics inevitably becomes part of the story. By acting as entrepreneurs in the political arena, innovators expand opportunities for themselves and for the public more generally, which would not have been likely if they had done things by the book.”
  8. “Dissenting through innovation can help make public officials more responsive to the people by reining in the excesses of the administrative state, making government more transparent and accountable, and ensuring that our civil rights and economic liberties are respected.”
  9. “In an age when many of the constitutional limitations on government power are being ignored or unenforced, innovation itself can act as a powerful check on the power of the state and can help serve as a protector of important human liberties.”
  10. “Lawmakers and regulators need to consider a balanced response to evasive entrepreneurialism that is rooted in the realization that technology creators and users are less likely to seek to evade laws and regulations when public policies are more in line with common sense.”

In a nutshell, the core arguments made in the book boil down to this: “evasive entrepreneurialism can transform our society for the better because it can do the following

  • Help expand the range of life-enriching innovations available to society.
  • Help citizens pursue lives of their own choosing—both as creators looking for the freedom to earn a living and as consumers looking to discover and enjoy important new goods and services.
  • Help provide a meaningful, ongoing check on government policies and programs that all too often have outlived their usefulness or simply defy common sense.”

I hope you will consider reading the book.

]]> https://techliberation.com/2020/04/28/evasive-entrepreneurs-10-highlights-from-the-book/feed/ 3 76698 Europe’s New AI Industrial Policy https://techliberation.com/2020/02/20/europes-new-ai-industrial-policy/ https://techliberation.com/2020/02/20/europes-new-ai-industrial-policy/#comments Thu, 20 Feb 2020 19:37:48 +0000 https://techliberation.com/?p=76667

The race for artificial intelligence (AI) supremacy is on with governments across the globe looking to take the lead in the next great technological revolution. As they did before during the internet era, the US and Europe are once again squaring off with competing policy frameworks.

In early January, the Trump Administration announced a new light-touch regulatory framework and then followed up with a proposed doubling of federal R&D spending on AI and quantum computing. This week, the European Union Commission issued a major policy framework for AI technologies and billed it as “a European approach to excellence and trust.”

It seems the EU basically wants to have its cake and eat it too by marrying up an ambitious industrial policy with a precautionary regulatory regime. We’ve seen this show before. Europe is doubling down on the same policy regime it used for the internet and digital commerce. It did not work out well for the continent then, and there are reasons to think it will backfire on them again for AI technologies.

An Ambitious Industrial Policy Vision

The new EU framework includes a lot of catchphrases and proposals that are an industrial policy lover’s dream. In an attempt to create “an ecosystem of excellence” and ensure the “human-centric development if AI,” it identifies a variety of existing or new industrial planning efforts, including: Digital Innovation Hubs, Enterprise Resource Planning, the Digital Europe Programme, the Key Digital Technology Joint Undertaking, and broad-based public private partnerships. This is all part of an official “Coordinated Plan” prepared together with the Member States “to foster the development and use of AI in Europe.”

To accomplish that, the Commission says it will “facilitate the creation of excellence and testing centres” that will “concentrate in sectors where Europe has the potential to become a global champion.” The Commission also wants to give special consideration to growing small and mid-size enterprises (SMEs) is establishing these plans.

Again, it’s an ambitious industrial policy vision, and one that will be accompanied by a wide variety of (yet-to-be-determined) regulatory enactments to shape the development and use of AI. But if that approach really works, why aren’t European digital companies global leaders today? Instead, firms based mostly in the US have risen to become household names across the globe. Regulation had an influence on that result because American firms enjoyed a policy regime that was rooted in “permissionless innovation,” which generally allows experimentation by default and addresses concerns by using more flexible, ex post remedies. By contrast, Europe’s internet policy approach was rooted in the precautionary principle, or the notion that innovation is essentially guilty until proven innocent. New technologies are to be subjected to prior constraints—or what the new European Commission white paper calls “prior conformity assessments”—before being allow into the wild.

Precautionary Regulation Dominates

Despite losing that last round of the innovation wars, the new EU white paper makes it clear that Europe will keep using a precautionary approach. What does that mean for AI regulation? The problem here begins with defining what is a “high-risk” AI application requiring prior restraints. The white paper defines it in a somewhat circular fashion, saying that, “an AI application should be considered high-risk where…(it) is employed in a sector where, given the characteristics of the activities typically undertaken, significant risks can be expected to occur” and is “used in such a manner that significant risks are likely to arise.” Instead of providing legal certainty, this definition clarifies almost nothing and will require future regulatory inquires to determine the full scope and nature of AI controls.

There’s also a lot of talk in the proposal about preemptively addressing “risks for fundamental rights,” which is understandable. AI innovations can raise various safety, security, and privacy concerns that deserve to be taken seriously. But what about the risk of not having access to important AI innovations at all? What about the risk of losing out on life-enriching—and in many cases life-saving—innovations because, instead of “building trust,” the regulatory regime builds the exact opposite: fear of innovating.

Entrepreneurs and investors respond to incentives. Before building or investing in a new technology, they want to know how long it will take to get that good or service launched—assuming they can get approval at all. Every innovator and investor factors such political risk into their business plans. When the potential costs of product launch overwhelm the likely benefits, they will abandon innovative efforts or look to engage in them elsewhere.

The EU says “the race for global leadership is ongoing,” and claims that, “Europe offers significant potential, knowledge and expertise” through its efforts to make the continent an AI innovation hub. Indeed, some of the best AI researchers are in Europe, and there are plenty of brilliant people brimming with entrepreneurial enthusiasm about creating world-class AI applications. But all that knowledge and enthusiasm do not matter much if the regulatory deck is stacked against innovation from the start.

And Even More Expansive Regulation Down the Road

Beyond the precautionary approach in that document, the EU’s accompanying white paper on safety and liability implications of AI leaves open the possibility of an expansion in preemptive regulatory requirements. “Additional obligations may be needed for manufacturers to ensure that they provide features to prevent the upload of software having an impact on safety during the lifetime of the AI products,” the document notes. Moreover, if an ongoing AI software update “modifies substantially the product in which it is downloaded, the entire product might be considered as a new product and compliance with the relevant safety product legislation must be reassessed at the time the modification is performed.”

That sort of regulatory regime may sound quite sensible at first blush. In practice, however, it means that every conceivable tweak to an algorithm requires costly and complex regulatory approval. If traditional computer software had required regulatory approval before any new modifications could be made, most consumers would still be stuck with an aol.com email address and Windows 95 as an operating system.

What the European Commission proves with its new AI policy framework is that it is easy to talk a big game about planning for an innovative future, but it is an entirely different thing to actually bring one about. The European approach will have clear competitive effects, or more specifically, anti-competitive effects. As is already the case with the EU’s regulatory approach to the data economy and GDPR in particular, regulatory compliance costs continue to skyrocket and small and mid-size enterprises struggle to cope. This means that only firms operating the largest digital platforms are able to shoulder these burdens, leaving consumers without as many competitive, low-cost choices as they might otherwise enjoy. Not even generous government support for SMEs will be able to counter-balance the costly entry barriers associated with over-regulation.

Solidifying Market Power of Existing Giants?

This is why it is so ironic how worried the EU is about the market power of Google, Facebook and other US-based tech giants: the regulatory burden now helps those firms maintain their market dominance. Over-regulation by the EU undermined both home-grown and international investment and competition that might challenge those existing players. With each addition layer of AI regulation that now gets piled on top of the Europe’s existing regulatory burden, the prospects for creative destruction decrease, as do the chances for life-enriching innovations to ever make it to consumers.

While the European Commission will, no doubt, insist that they are implementing this new AI regime with the very best of intentions in mind, there is no escaping the fact that regulation involves complex trade-offs and unforeseeable consequences. The consequences in this case are likely a bit easier to predict, however: By smothering new AI applications in layers of red tape, we can expect fewer innovations and less competition.

Despite all the talk of boosting SMEs, perhaps the EU will eventually become more like China and unabashedly support larger home-grown firms to make sure they are part of the global AI race. China has already made waves on this front with its 2017 “New Generation Artificial Intelligence Development Plan,” an audacious industrial policy plan which seeks “to build China’s first-mover advantage in the development of AI [and] to accelerate the construction of an innovative nation and global power in science and technology.” The document is as much a manifesto about geopolitical power as it is about technological governance. And it does not try to hide China’s authoritarian impulse to meticulously plan every facet of daily life under the auspices of promoting global technological leadership. China’s AI manifesto even concludes with a section on “public opinion guidance” that creepily insists the country will, “Fully use all kinds of traditional media and new media to quickly propagate new progress and new achievements in AI, to let the healthy development of AI become a consensus in all of society, and muster the vigor of all of society to participate in and support the development of AI.”

The new European AI industrial policy framework does not go as far as China’s, not only because the continent is obviously more open and democratic by nature, but also because the EU is a collection of many countries and cultures that will never be able to speak as coherently and forcefully with one voice on all technological governance matters. In fact, the EU’s new governance framework explicitly leaves room for more tailored AI regulation by individual member states.

Conclusion

This leaves Europe stuck between the polar opposites of China and the US when it comes to AI governance. China’s meticulously detailed, highly centralized, state-driven approach stands in stark contrast to the more bottom-up, adaptive American approach which insists that regulators, “must avoid a precautionary approach that holds AI systems to such an impossibly high standard that society cannot enjoy their benefits.”

The US approach also leans heavily on “soft law,” or informal governance mechanisms that are not as burdensome as precautionary regulatory controls. Soft law can include a wide variety of tools and methods for addressing policy concerns, including multistakeholder initiatives, best practices and standards, agency workshops and guidance documents, educational efforts, and much more. These are the governance tools the dominated for the internet and digital platforms for that past twenty years in the US, and they will likely continue to be the primary governance mechanisms for artificial intelligence, robotics, the internet of things, and other emerging tech sectors.

The EU probably thinks it has found the Goldilocks formula and gotten AI policy just right by falling between China and the US on the governance spectrum. It is more likely, however, that European policymakers will be unable to resist the urge to over-plan and micro-manage AI markets until they are once again left wondering how they got stuck trying to regulate market leaders that are headquartered oceans away from them. With the US once again adopting a more flexible approach, we could see a replay of the Web Wars, with innovators and investors putting their efforts behind AI launches in the US instead of Europe. Meanwhile, China will likely attract far more global venture capital for AI and robotics launches than they did for digital platforms. This could really put the squeeze on Europe.

Only time will tell. But, to paraphrase Yoda, when it comes to global artificial intelligence governance, one thing is clear: Begun the AI war has.

]]> https://techliberation.com/2020/02/20/europes-new-ai-industrial-policy/feed/ 1 76667 Congress as a Non-Actor in Tech Policy https://techliberation.com/2020/02/04/congress-as-a-non-actor-in-tech-policy/ https://techliberation.com/2020/02/04/congress-as-a-non-actor-in-tech-policy/#comments Tue, 04 Feb 2020 19:28:42 +0000 https://techliberation.com/?p=76658

ImageCongress has become a less important player in the field of technology policy. Why did that happen, and what are the ramifications for technological governance efforts going forward?

I’ve spent almost 30 years covering technology policy. There was a time in my life when I spent almost all my time as a policy analyst preoccupied with developments in the federal legislative arena. I lived in the trenches of Capitol Hill and interacted with lawmakers and their staff morning, noon, and night.

In recent years, however, I have spent very little time focused on the Legislative Branch because it has effectively become a non-actor on technology policy. It is not that congressional lawmakers stopped caring about tech policy. Interest actually remains quite high—perhaps higher than ever before. Congress also continues to introduce lots of bills, host plenty of hearings, and issue mountains of press releases related to tech policy issues.

Nonetheless, all that interest and activity has not really translated into much important legislation. While it is hard to track tech-oriented legislative trends statistically because of the complication of defining “technology policy” over time, judged by substantive output, Congress has largely checked out of technological policymaking.

Think about digital privacy. How many years now have people been predicting a comprehensive “baseline” privacy bill would pass in each legislative session? It never happens. Perhaps it will this year, but if you would like to place a wager on it, I will take that bet.

Speaking of bets, for several years now, I have been wagering with friends that Congress will not pass federal legislation creating a national autonomous vehicles framework. Each session I win that bet. Keep in mind, a framework for driverless cars is far less controversial than privacy policy. Still, nothing substantive ever gets done in Congress.

Same goes for cybersecurity with lots of calls for big measures, but no final action. Folks are now also telling me to expect a big artificial intelligence bill one day soon. I sincerely doubt it. Again, I’ll bet on it if you’d like to lose some money!

Let me be clear, there may actually be some very good reasons why Congress should implement a national framework for privacy, driverless cars, and some AI policy issues. But all the wishful thinking in the world will not magically make it happen.

We need to entertain the possibility that Congress has largely checked out of the world of substantive tech policymaking and isn’t coming back. We may get a few big surprise measures here and there, as we did with clumsily-drafted FOSTA-SESTA. If anything, it is more likely that we instead see misguided legislative riders attached to non-germane measures during late night negotiations. But even haphazard efforts like those will be extremely rare. The days of Congress passing big bills like the Telecom Act of 1996 or the Cable Act of 1992 appear mostly over.

Why Congress Is No Longer the Major Player It Once Was

I think there are probably many obvious explanations for why Congress has checked out of tech policymaking, but let me try to boil it down to a couple of interrelated trends:

The “pacing problem” has intensified: The pacing problem refers to the inability of legal or regulatory regimes to keep adjust to the intensifying pace of technological change. There are just more emerging technologies than ever, and they are evolving faster than ever, too. “New technologies that used to have two-year cycle times now can become obsolete in six months, and the pace of change is not slowing,” says consulting firm Deloitte.

A growing multiplicity of technologies means more tech policy issues to cover. And those issues grow more complicated each year. As soon as lawmakers wrap their heads around one technology (if they do at all), another innovation pops up that complicates things further or crowds out their attention.

Technological convergence and blurring governance boundaries: Technology policymaking increasingly involves metaphysical questions about the underlying nature of things. For example, what is a “phone,” a “medical device,” or an “aerial vehicle”? These things used to be relatively easy to define and had well-understood meanings in federal statutes and regulations. But those concepts evolved rapidly in an age of widespread technological convergence and rapid-fire “combinatorial innovation,” with new technologies multiplying and building on top of one another in the symbiotic fashion. Basically, almost as soon as new tech laws or regulations are enacted, they are confronted with new marketplace realities and technological changes that call into question legal classifications or regulatory distinctions.

For example, today’s smartphones combine dozens of different functions that were previously quite distinct, including health tracking capabilities, mobile payment systems, and video distribution, all of which remain heavily regulated by an assortment of federal laws and agencies. But the convergence of all these capabilities in a single device that we can carry in our pockets creates massive governance challenges, not only for archaic legislative frameworks, but even for newer semantic distinctions that may seem current one moment only to be obliterated the next. These factors also make it harder to figure out who in Congress should be driving policy because technological convergence blurs previously distinct governance categories among legislative committees and the laws they have crafted.

Legislative dysfunctionalism: Policymaking processes move slowly by design. Constitutional constraints and other legal requirements demand it. But things move even slower today because of what Jonathan Rauch calls “demosclerosis,” or the “government’s progressive loss of the ability to adapt.” “[A]s layer is dropped upon layer,” he argued, “the accumulated mass becomes gradually less rational and less flexible.”

Inadequate resources are also part of the problem with Congress facing a complex, rapidly-evolving set of issues but devoting only limited resources to technical staff or studies to better understand these developments. This combined with the factors cited above has led to a never-ending “competency trap,” with lawmakers and their staffs seemingly always one step behind technological developments and societal demands or expectations.

Meanwhile, partisanship increases and the work load on many other fronts grows alongside it. There’s just a lot more on Congress’s plate than ever before. Plus, tech policy matters seemingly always take a back seat to tax, budget, entitlements, defense, and other issues.

Many people hope that boosting technology assessment efforts might help correct these problems. Perhaps better technical advice could help lawmakers ask less ignorant questions at tech-oriented congressional hearings, which have become showcases for the staggering lack of congressional understanding of modern technologies. But just adding new technology assessment capacity, such as in the form of a revived Office of Technology Assessment, won’t likely move the needle much in terms of actual legislative output. More serious structural reforms will be required.

Globalization: Many modern technologies “are truly global and call out for policy approaches that do not respect traditional national borders,” note former NITA officials Lawrence E. Strickling and Jonah Force Hill. Congress only has so much control over technologies that defy national boundaries, further complicating tech governance questions.

Yet, one would think that when America’s global competitive advantage was on the line, Congress would have greater reason to assert itself and craft frameworks to ensure US firms are not disadvantaged by a lack of policy clarity. That has not proven to be the case, however. Congressional lawmakers do plenty of huffing and puffing about the tech governance choices made by Europe, China, and other governments, but they then leave the field wide open to them (as well as lower levels of government) to craft policies that govern national markets throughout the United States.

Endless delegation: Speaking of passing the buck, Congress has been doing it for decades on tech policy by delegating massive and quite amorphous authority to technocratic administrative agencies. Over the past half century, scholars from various disciplines—economics, law, political science, history, and others—have explored the growth of what has been alternatively called the “interest group society,”  “receivership by regulation,”  “iron triangles,” and “client politics.” This literature identifies the way Congress has increasingly abdicated its constitutional role as lawmaker by shifting hard policy questions to regulatory agencies and then hoping that bureaucrats could figure out all the answers.

Delegation is even more common for the most technical policy matters, and that trend has only accelerated in recent years as the complexity increases and overwhelms lawmakers and their staff.

Ramifications for Tech Governance Going Forward

If Congress remains largely incapable of ever getting the ball over the goal line on important tech policy matters, what are some of the ramifications? There are many, but I will identify just a few of the most obvious ones:

  • More tech-oriented legislative activity will shift to the states: In fact, it already has. For each of the tech policy issues I identified earlier (privacy, driverless cars, cybersecurity, and even some AI-related issues like facial recognition), states are—for better or worse—picking up the slack. We should expect that trend to accelerate. This will create an increasingly confusing patchwork of policies that will potentially raise serious barriers to entry and innovation. Nonetheless, I can’t see this trend reversing anytime soon. Perhaps Congress will finally act on privacy or driverless cars legislation if for no other reason than to preempt a crazy-quilt of contradictory policies. Of course, that’s what people have been predicting for years, and it never happens.
  • “Soft law” becomes the dominate governance force for tech: Again, it already has. Soft law refers to informal, collaborative, and constantly evolving governance mechanisms that differ from hard law in that they lack the same degree of enforceability. Soft law can include things like multi-stakeholder processes, industry best practices and standards, agency workshops and guidance documents, and educational efforts. But that just scratches the surface of soft law mechanisms. For better or worse, soft law is becoming the dominant modus operandi for most modern technological governance. We can expect that trend to accelerate to fill the governance gap left by Congressional inaction. For example, we don’t have any formal “rules of the road” for driverless cars, but we do now have four iterations of Department of Transportation guidance on driverless cars. Version 4.0of the DoT guidance for automated vehicles was just released this month. Expect the “soft law-ization” of technological governance to expand considerably in coming years because it is really the only way for agencies to cope with the pacing problem and those metaphysical issues identified earlier. Because soft law is not boxed in by rigid preconceptions of what a particular technology or technological process is or entails, it is often better able to address new marketplace realities. Soft law can adapt as technologies do. With Congress out of the picture, it will have to.
  • The congressional tech policy death spiral accelerates. Some may think (or at least hope) that the situation described here can’t get any worse. To the contrary, it can get radically worse. With our politics increasingly infected with bitter partisanship and rancor, what are the chances that lawmakers can work together to craft comprehensive tech policy measures? I’d say the odds are approaching zero. The Cable Act, the Telecom Act (and Sec. 230), and the Internet Tax Freedom Act all enjoyed broad, bipartisan support when they passed in the 1990s. People reached across the aisle to get things done. It didn’t always work, and sometimes it resulted in misguided policies (like the Communications Decency Act’s provisions trying to censor internet “indecency”). But bipartisan lawmaking scenarios like those seem almost unthinkable now. To the extent many lawmakers even show up at tech-oriented congressional hearings anymore, it is mostly to score points in front of the cameras for Team Red or Team Blue back home. Serious legislative oversight and policymaking is dead; it’s mostly just show-trials and media circuses at this point.

Should I Care about Congress Anymore?

If you believe this miserable thesis is correct but continue to focus on the Legislative Branch for a living, you may be asking yourself: Am I wasting all my time here? Not necessarily. Congress is still actively interested in tech policy matters. For those who hope to limit that damage Congress might do by hastily passing ham-handed, crisis-driven policy measures, your efforts in the trenches will continue to be important in curbing the worst instincts of some lawmakers. In many instances, preserving a perpetual stalemate may go down as a tremendous victory.

For example, as the debate over Section 230 intensifies—with politicians of all stripes looking to gut the most important of all Internet freedom policies—it is vital that smart people work with lawmakers and their staff to beat back misguided and destructive measures. Hopefully this becomes another instance of legislative gridlock winning out! And I think it will.

More realistically, your role will not be to stop Congress from doing insanely destructive things, it will be to just stop them from saying those things. In fact, that seems to be what a lot of people who work with Congress already do today. When I chat with various inside-the-Beltway policy advocates and industry reps today, they usually acknowledge that the prospects for actual legislation on any given issue are quite slim. They will, of course, continue to try to work with lawmakers, their committees, and their staff to either advance or stop legislative measures. Yet, they all seem to accept the utter futility of it all.

Why do they persist? Most obviously, they want to at least preserve the legislative stalemate and not cede the ground to their enemies who might succeed in getting lawmakers to do something if only one side was communicating with Congress.

But the other thing these policy advocates are hoping to achieve is better messaging. Regulatory advocates want lawmakers to use the power of the bully pulpit to put pressure on various people or groups to change behavior, even in the absence of any legislative action. By contrast, many in industry want to make sure that their technologies are understood and not endlessly demonized. Bad press isn’t good for business, even if all the congressional threats never result in final legislation. Also, those defending innovation more generally will want to make sure that even if lawmakers aren’t making any actual laws, they still better understand and appreciate the importance of new technological capabilities for improving human welfare.

Those are all good reasons not to give up your legislative advocacy. For some of us, however, the personal cost-benefit analysis just doesn’t add up. Our focus has shifted to where the real action is at: federal administrative agencies, statehouses and state administrative agencies, the courts, and the growing world of multi-stakeholder governance and other soft law efforts. Congress has checked out, but technological governance lives on in many other forms and venues.

]]> https://techliberation.com/2020/02/04/congress-as-a-non-actor-in-tech-policy/feed/ 1 76658 Podcast on Driverless Cars, AI & “Soft Law” Governance https://techliberation.com/2020/01/21/podcast-on-driverless-cars-ai-soft-law-governance/ https://techliberation.com/2020/01/21/podcast-on-driverless-cars-ai-soft-law-governance/#comments Tue, 21 Jan 2020 16:55:06 +0000 https://techliberation.com/?p=76652

Here’s a new Federalist Society Regulatory Transparency “Tech Roundup” podcast about driverless cars, artificial intelligence and the growth of “soft law” governance for both. The 34-minute podcast features a conversation between Caleb Watney and me about new Trump Administration AI guidelines as well as the Department of Transportation’s new “Version 4.0” guidance for automated vehicles.

This podcast builds on my recent essay, “Trump’s AI Framework & the Future of Emerging Tech Governance” as well as an earlier law review article, “Soft Law for Hard Problems: The Governance of Emerging Technologies in an Uncertain Future.”

]]> https://techliberation.com/2020/01/21/podcast-on-driverless-cars-ai-soft-law-governance/feed/ 1 76652 Trump’s AI Framework & the Future of Emerging Tech Governance https://techliberation.com/2020/01/08/trumps-ai-framework-the-future-of-emerging-tech-governance/ https://techliberation.com/2020/01/08/trumps-ai-framework-the-future-of-emerging-tech-governance/#respond Wed, 08 Jan 2020 20:04:57 +0000 https://techliberation.com/?p=76648

This week, the Trump Administration proposed a new policy framework for artificial intelligence (AI) technologies that attempts to balance the need for continued innovation with a set of principles to address concerns about new AI services and applications. This represents an important moment in the history of emerging technology governance as it creates a policy vision for AI that is generally consistent with earlier innovation governance frameworks established by previous administrations.

Generally speaking, the Trump governance vision for AI encourages regulatory humility and patience in the face of an uncertain technological future. However, the framework also endorses a combination of “hard” and “soft” law mechanisms to address policy concerns that have already been raised about developing or predicted AI innovations.

AI promises to revolutionize almost every sector of the economy and can potentially benefit our lives in numerous ways. But AI applications also raise a number of policy concerns, specifically regarding safety or fairness. On the safety front, for example, some are concerned about the AI systems that control drones, driverless cars, robots, and other autonomous systems. When it comes to fairness considerations, critics worry about “bias” in algorithmic systems that could deny people jobs, loans, or health care, among other things.

These concerns deserve serious consideration and some level of policy guidance or else the public may never come to trust AI systems, especially if the worst of those fears materialize as AI technologies spread. But how policy is formulated and imposed matters profoundly. A heavy-handed, top-down regulatory regime could undermine AI’s potential to improve lives and strengthen the economy. Accordingly, a flexible governance framework is needed and the administration’s new guidelines for AI regulation do a reasonably good job striking that balance.

Background

Last February, the White House issued Executive Order 13859, on “Maintaining American Leadership in Artificial Intelligence.” The Order announced the creation of the “American AI Initiative,” an effort to “focus the resources of the Federal government to develop AI.” It prioritized investments in AI-focused research and development (R&D), building a workforce ready for the AI era, international engagement on AI priorities, and the establishment governance standards for AI systems to “help Federal regulatory agencies develop and maintain approaches for the safe and trustworthy creation and adoption of new AI technologies.”

Regarding that last objective, Order 13589 required the Office of Management and Budget (OMB) and the Office of Science and Technology Policy (OSTP) to develop a framework and set of principles for federal agencies to follow when considering the development of regulatory and non‑regulatory approaches for AI. Importantly, the Order also specified that the framework should seek to “advance American innovation” and “reduce barriers to the use of AI technologies in order to promote their innovative application while protecting civil liberties, privacy, American values, and United States economic and national security.”

That resulted in the memorandum sent to heads of federal departments and agencies this week entitled, “Guidance for Regulation of Artificial Intelligence Applications” (hereinafter AI Guidance). The draft version of the AI Guidance specifies that “federal agencies must avoid regulatory or non-regulatory actions that needlessly hamper AI innovation and growth.” More specifically:

“Agencies must avoid a precautionary approach that holds AI systems to such an impossibly high standard that society cannot enjoy their benefits. Where AI entails risk, agencies should consider the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace.”

But the AI Guidance is certainly not a call for comprehensive deregulation or the abandonment of all AI federal oversight. The memorandum’s very title reflects an understanding that existing laws and agency rules will continue to play a role in guiding the development of AI, machine-learning, and autonomous systems.

Accordingly, and consistent with past executive orders and OMB regulatory guidance documents for federal agencies, the AI Guidance establishes a set of ten principles that agencies must take into consideration when considering AI policy:

  1. Public trust in AI: Requiring that “the government’s regulatory and non-regulatory approaches to AI promote reliable, robust, and trustworthy AI applications, which will contribute to public trust in AI.”
  2. Public participation: Agencies must provide “ample opportunities for the public to provide information and participate in all stages of the rulemaking process.”
  3. Scientific integrity and information quality: Agencies should “leverage scientific and technical information and processes” to build trust and ensure data quality and transparency.
  4. Risk assessment and management: Acknowledging that “all activities involve tradeoffs,” the AI Guidance requires that “a risk-based approach should be used to determine which risks are acceptable and which risks present the possibility of unacceptable harm, or harm that has expected costs greater than expected benefits.”
  5. Benefits and costs: As part of those risk assessments, agencies must “carefully consider the full societal costs, benefits, and distributional effects before considering regulations related to the development and deployment of AI applications. Such consideration will include the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace, whether implementing AI will change the type of errors created by the system, as well as comparison to the degree of risk tolerated in other existing ones.”
  6. Flexibility: OMB encourages agencies to “pursue performance-based and flexible approaches that can adapt to rapid changes and updates to AI applications.”
  7. Fairness and non-discrimination: Acknowledging that “in some instances, introduce real-world bias that produces discriminatory outcomes or decisions that undermine public trust and confidence in AI,” the AI Guidance requires agencies to consider “issues of fairness and non-discrimination with respect to outcomes and decisions produced by the AI application at issue.”
  8. Disclosure and transparency: Agencies are encouraged to consider how greater “transparency and disclosure can increase public trust and confidence in AI applications.”
  9. Safety and security: Agencies are required to “promote the development of AI systems that are safe, secure, and operate as intended, and encourage the consideration of safety and security issues throughout the AI design, development, deployment, and operation process.”
  10. Interagency coordination: The guidance makes it clear that a “coherent and whole-of-government approach to AI oversight requires interagency coordination.”

Soft Law Ascends

Importantly, the AI Guidance also encourages agencies to be open to “non-regulatory approaches to AI” governance and specifies three particular models:

  • Sector-specific policy guidance or frameworks: OSTP writes that “agencies should consider using any existing statutory authority to issue non-regulatory policy statements, guidance, or testing and deployment frameworks, as a means of encouraging AI innovation in that sector.” The memorandum also notes that this can include “work done in collaboration with industry, such as development of playbooks and voluntary incentive frameworks.”
  • Pilot programs and experiments: The document encourages the use of “pilot programs that provide safe harbors for specific AI applications” which “could produce useful data to inform future rulemaking and non-regulatory approaches.”
  • Voluntary consensus standards: Before regulating, the AI Guidance encourages agencies to consider how voluntary consensus standards, assessment programs, and compliance programs might be used to address policy concerns.

These represent “soft law” approaches to technological governance and they are becoming all the rage in technology policy discussions today. Soft law mechanisms are informal, collaborative, and constantly evolving governance efforts. While not formerly binding like “hard law” rules and regulations, soft law efforts nonetheless create a set of expectations about sensible development and use of technologies. Soft law can include multistakeholder initiatives, best practices and standards, agency workshops and guidance documents, educational efforts, and much more.

Soft law has become the dominant governance approach for emerging technologies because it is often better able to address the “pacing problem,” which refers to the growing gap between the rate of technological innovation and policymakers’ ability to keep up with it. As I have previously noted, the pacing problem is “becoming the great equalizer in debates over technological governance because it forces governments to rethink their approach to the regulation of many sectors and technologies.”

Not only do traditional legislative and regulatory hard law systems struggle to keep up with fast-paced technological changes, but oftentimes those older mechanisms are just too rigid and unsuited for new sectors and developments. That is definitely the case for AI, which is multi-dimensional in nature and even defies easy definition. Soft law offers a more flexible, adaptive approach to learning on the fly and cobbling together principles and policies that can address new policy concerns as they develop in specific contexts, without derailing potentially important innovations.

Building on Past Governance Frameworks

In this sense, the Trump administration’s AI Guidance borrows from past policy frameworks by marrying up a desire to promote an exciting new set of emerging technologies alongside the need for reasonable but flexible oversight and governance mechanisms. At a high level, the AI Guidance builds on many of the same principles that motivated the Clinton administration’s Framework for Global Electronic Commerce, a statement of principles and policy objectives for the then-emerging Internet. The document, which was issued in July 1997, said that “governments should encourage industry self-regulation and private sector leadership where possible” and “avoid undue restrictions on electronic commerce.”

The Framework was a clean break from the top-down regulatory paradigm that had previously governed traditional communications and media technologies. Clinton’s Framework insisted that, to the extent government intervention was needed at all, “its aim should be to support and enforce a predictable, minimalist, consistent and simple legal environment for commerce.” The use of soft law and multistakeholder models was a key component of this vision, and those more flexible governance approaches were tapped by the subsequent administrations to address emerging tech policy concerns.

For example, the Obama administration considerably expanded the use of multistakeholder mechanisms and other soft law tools in response to the need of oversight of fast-moving technologies. The Obama administration had many different policy governance efforts underway for specific AI technologies and concerns, including workshops and multistakeholder efforts focused on the safety, security, and privacy-related issues surrounding “big data” systems, online advertising, connected cars, drones, and more.

Whereas the Obama administration was deeper in the weeds of the policy issues associated with specific AI and machine-learning applications, the Trump administration has sought to both build on those focused efforts while also stepping back to consider AI governance at the 30,000-foot level. In essence, the AI Guidance combines some of the aspirational elements found in the Clinton Framework alongside the Obama administration’s more targeted approach to consider specific policy concerns across many different sectors and technologies.

Trump’s AI Guidance adds an element of formality to this process regarding how federal agencies should address AI developments and formulate potential policy responses. It does so by counseling humility and even potential forbearance until all the facts are in. “Fostering innovation and growth through forbearing from new regulations may be appropriate,” the memorandum says. Agencies should consider new regulation only after they have reached the decision, in light of the foregoing section and other considerations, that Federal regulation is necessary.” Again, this is very much consistent with more general regulatory guidance issued by every administration since President Reagan was in office.

Flexible, Adaptive Governance is Key

The AI Guidance foreshadows the future of not only AI governance but the governance of many other emerging technologies. Hard law will continue to provide a backstop and have a role in guiding technological developments. Toward that end, efforts like the new AI Guidance are important because it represents an effort to “regulate the regulators” by placing some ground rules on how they go about applying old law to new developments.

But soft law governance is where the real action is at, both for AI and almost all emerging technologies today. The Trump AI Guidance reflects the extent to which soft law has become the dominant governance paradigm for modern tech sectors. As my colleagues Jennifer Huddleston and Trace Mitchell have noted, soft law is already effectively the law of the land for driverless cars, for example. After years of congressional wrangling over a federal autonomous vehicle regulatory framework—one that has widespread bipartisan support, no less—we still do not have a law on the books. Instead, the Department of Transportation has been cobbling together informal “rules of the road” through informal guidance documents that have been “versioned” as if they were computer software (i.e., Version 1.0, 2.0, 3.0). Version 4.0 of the DoT guidance for automated vehicles was just released this week.

That is the same approach that the National Institute of Standards and Technology (NIST) has taken with the privacy guidelines it developed. NIST’s Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management is also versioned like software. And many other federal agencies, especially the Federal Trade Commission, have tapped a wide variety of soft law tools—such as agency workshops and workshop reports that recommended privacy best practices for various technologies. Meanwhile, the National Telecommunications and Information Administration (NTIA) has used multistakeholder processes to address privacy concerns surrounding a wide range of technologies, including drones and facial recognition. NIST, FTC, and NTIA have undertaken these informal governance efforts because, despite over a decade of debate, Congress still has not advanced comprehensive federal privacy legislation. For better or worse, soft law has filled that governance gap.

Addressing Likely Objections from Left & Right

Many people of varying ideological dispositions will object to the growing role of soft law as the primary governance tool for emerging technology policy. Some conservatives will cringe at the sound of giving regulators greater leeway to address amorphous policy concerns, fearing that it will result in unconstrained exercises of unaccountable, extra-constitutional power.

Some of those concerns are valid, but they fail to account for the fact that the prospects for agency downsizing or deregulation they prefer are extremely limited. Practically speaking, the administrative state isn’t going anywhere. In some cases, agencies can actually do some real good by encouraging innovators to think about how to “bake-in” sensible best practices to preemptively address concerns about the privacy, safety, security, and fairness of various AI systems. Better those concerns be addressed in more flexible, adaptive fashion than by a heavy-handed, overly-rigid regulatory approach. Soft law offers that possibility, even if legitimate concerns remain about agency accountability and transparency.

Many to the left of center will be critical of this governance approach as well, but on very different grounds. As Associated Press reporter Matt O’Brien notes, “the vagueness of the principles announced by the White House is unlikely to satisfy AI watchdogs who have warned of a lack of accountability as computer systems are deployed to take on human roles in high-risk social settings, such as mortgage lending or job recruitment.”

These concerns actually are addressed in several of the OSTP’s ten principles, including those which stress the need for fairness and non-discrimination, information quality, public participation, disclosure and transparency, and safety and security. Yet many on the left will claim these principles merely pay lip service to these values and that what is really needed is a full-blown regulatory regime and some sort of corresponding new federal AI agency, which would preemptively determine which AI technologies would be allowed into the wild.

Already, an Algorithmic Accountability Act was introduced in Congress last year that would ask the FTC to take a more active role in policing “inaccurate, unfair, biased, or discriminatory decisions impacting consumers” that may have resulted from “automated decision systems.” Meanwhile, some academics have called for the creation of a Federal Robotics Commission or a National Algorithmic Technology Safety Administration to preemptively oversee new AI developments.

The problem with overly-precautionary regulation of that sort could potentially unduly limit AI innovation and the many benefits it entails. There may be some AI applications that pose serious and immediate risks to humanity and which require preemptive restraints on their development and use. Autonomous military and law enforcement applications are the most obvious examples. But most AI applications do not rise to that same level of regulatory concern, and other governance approaches are required to balance the use and misuse of them. This is why a more open and flexible governance approach is needed. Moreover, the old regulatory system just cannot keep up anymore, and it is ill-suited to address most policy concerns in a timely or efficient fashion.

Cristie Ford, and advocate of greater regulatory oversight for fintech, notes in her latest book that the problem with “old-style Welfare State regulation” is that it is “a clumsy, blunt instrument for achieving regulatory objectives” due to its reliance upon “one-size-fits-all mandates, prohibitions, and penalties.” Ford acknowledges what many other regulatory advocates are reluctant to admit:  public policies toward fast-paced technology sectors can no longer be governed effectively using the Analog Era’s top-down, command-and-control regulatory processes. Far too many federal agencies rely on a “build-and-freeze model” of regulation that puts rules in stone to deal with one sets of issues one day, but then either fails to eliminate them later when they become obsolete or to reform those rules to bring them in line with new social, economic, and technical realities.

If we hope to encourage continued innovation in sectors that could produce profoundly important, life-enriching technologies, America’s regulatory approach for AI and emerging technology needs to move away from “build-and-freeze” and toward “build-and-adapt.” Regulation is still needed, but the old regulatory toolkit is badly broken. For better or worse, soft law is going to fill the resulting governance gap, regardless of objections from some on the left or the right. Pragmatic policymaking is going to carry the day for emerging technology governance.

Conclusion

The Trump Administration AI Guidance represents a continuation and extension of this trend toward more flexible, adaptive governance approaches for emerging technologies. It offers a pragmatic vision that builds on the policies and paradigms of the past, while also encouraging fresh thinking about how best to balance the need for continued innovation alongside the various concerns about disruptive technological change.

There are many challenging issues that lie ahead and the new AI Guidance cannot provide bright-line answers to all the hypothetical questions that people want answered today. No one possesses a crystal ball that will allow them to forecast the technological future. Only ongoing trial-and-error experimentation and policy improvisation will allow us to find sensible solutions. A policy approach rooted in humility, flexibility, and forbearance will help ensure that America’s regulatory policies continue to promote both innovation and the public good.

]]> https://techliberation.com/2020/01/08/trumps-ai-framework-the-future-of-emerging-tech-governance/feed/ 0 76648 The Pacing Problem and the Future of Technology Regulation https://techliberation.com/2018/08/10/the-pacing-problem-and-the-future-of-technology-regulation/ https://techliberation.com/2018/08/10/the-pacing-problem-and-the-future-of-technology-regulation/#respond Fri, 10 Aug 2018 12:48:10 +0000 https://techliberation.com/?p=76342

[first published at The Bridge on August 9, 2018]

What happens when technological innovation outpaces the ability of laws and regulations to keep up?

This phenomenon is known as “the pacing problem,” and it has profound ramifications for the governance of emerging technologies. Indeed, the pacing problem is becoming the great equalizer in debates over technological governance because it forces governments to rethink their approach to the regulation of many sectors and technologies.

The Innovation Cornucopia

Had Rip Van Winkle woken up his famous nap today, he’d be shocked by all the changes around him. At-home genetics tests, personal drones, driverless cars, lab-grown meats, and 3D-printed prosthetic limbs are just some of the amazing innovations that would boggle his mind. New devices and services are flying at us so rapidly that we sometimes forget that most did not even exist a short time ago. At this point, it feels like our smartphones have been in our lives forever, but even just a decade ago, very few of us had one. Likewise, plenty of people now regularly enjoy the benefits of the sharing economy, but ten years ago, Uber, Lyft, and Airbnb did not even exist. Most of the social networking platforms or online video and audio streaming services that we use today had not even been created 15 years ago. Back then, Netflix’s DVD mail subscription service seemed downright revolutionary.

With every innovation comes more questions about how the law should keep pace, or whether it even can. “There has always been a pacing problem,” observes Yale University bioethicist Wendell Wallach, author of  A Dangerous Master: How to Keep Technology from Slipping beyond Our Control. But what Wallach and many other scholars worry about today is that the pace of change has been kicked into overdrive, making it more difficult than ever for traditional legal schemes and regulatory mechanisms to stay relevant. Larry Downes refers to this as “The Law of Disruption.” In his 2009 book on this “law,” Downes showed how “technology changes exponentially, but social, economic, and legal systems change incrementally” and that this law was becoming “a simple but unavoidable principle of modern life.”

Moore’s Law Quickens the Pace

There are three primary reasons the pacing problem is such a force in our modern world. The root cause lies in the power of “combinatorial innovation,” which is driven by “Moore’s Law.”  The Information Revolution spawned a stunning array of new technological capabilities that build on top of one another in a symbiotic fashion. Think about the shared foundational elements of most modern inventions: microchips, sensors, digital code, big data, cloud computing, remote data storage, wireless networking and geolocation capabilities, machine-learning, cryptography, and more. Each of these underlying capabilities is becoming faster, cheaper, smaller, more powerful, and easier to find and use. Innovators are combining them as part of their ongoing search for new and better ways of doing things.

Moore’s Law powers these developments. Moore’s Law is the principle named after Intel co-founder Gordon E. Moore, who first observed in 1965 that “computing would dramatically increase in power, and decrease in relative cost, at an exponential pace” in coming years. Indeed, it has continued to do so for the past half century for many information technologies. A recent Technology Policy Institute white paper noted that “data transit prices fell from about $1200 per Mbps in 1998 to $0.02 per Mbps in 2017.”

These forces are now revolutionizing other sectors as “software eats the world” and innovators utilize these new technologies to address nearly every conceivable need and want. In the field of genetics, the biological equivalent of Moore’s Law is known as the “Carlson curve.” The past two decades have seen the cost of sequencing a human genome fall from over $100 million to under $1,000, a rate nearly three times faster than Moore’s Law.

What the Public Wants, the Public Gets

The second reason the pacing problem is accelerating is that the public wants it to! It is true that many people say they are uneasy with many emerging technologies. When new gadgets and services first gain attention, a “technopanic” attitude often ensues. That is unsurprising because, as others have noted, “fear has gone hand in hand with technological advancements throughout history.”

But societal attitudes toward technological change often shift rapidly. They do so even faster today as citizens quickly assimilate new tools into their daily lives and then expect that even more and better tools will be delivered tomorrow. As more people begin to realize how new technologies improve our lives in meaningful ways, it becomes extremely hard for policymakers to take those innovations away or even tell us not to expect better ones. This relationship between technological change and societal expectations acts as an extraordinarily powerful check on the ability of regulators to “roll back the clock” on innovative activities.

Broken Government Exacerbates the Problem

Finally, the pacing problem is becoming more acute because “demosclerosis” and “kludgeocracy” have taken hold within American government. Jonathan Rauch coined the term demosclerosis in his 1999 book Government’s End: Why Washington Stopped Working to describe “government’s progressive loss of the ability to adapt.” “[A]s layer is dropped upon layer,” he argued, “the accumulated mass becomes gradually less rational and less flexible.”

Instead of cleaning up old legalistic messes and adapting to the times, government solutions are more often clumsily cobbled together to patch past problems and create temporary solutions. Steven Teles refers to this as kludgeocracy. “The complexity and incoherence of our government often make it difficult for us to understand just what that government is doing,” Teles says. Kludgeocracy creates serious costs for individual citizens, governments themselves, and to our democratic systems more generally, he argues. Taken together, demosclerosis and kludgeocracy breed highly dysfunctional governments and make it even easier for the pacing problem to speed ahead.

Can Policymakers Adapt?

Regulators are not oblivious to the challenges posed by the pacing problem. “I have said more than once that innovation moves at the speed of imagination and that government has traditionally moved at, well, the speed of government,” remarked Michael Heurta, head of the Federal Aviation Administration, in a 2016 speech regarding drones. Shortly after Huerta made those comments, the Department of Transportation released a report on the regulation of driverless car technology which noted that “The speed with which [driverless cars] are advancing, combined with the complexity and novelty of these innovations, threatens to outpace the Agency’s conventional regulatory processes and capabilities.”

Food and Drug Administration (FDA) regulators have increasingly referenced the pacing problem when discussing the challenge of keeping up with new medical innovations.  The New York Times recently asked Dr. Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research, how the agency planned to deal with hundreds of “rogue” stem cell treatment clinics. “There are hundreds and hundreds of these clinics,” he said. “We simply don’t have the bandwidth to go after all of them at once.”

The pacing problem has even crept into antitrust enforcement. The US Department of Justice (DOJ) sought to break up Microsoft in the late 1990s, but as the legal proceedings dragged on through the early 2000’s, the market moved and made the DOJ’s case moot. Google Chrome and Mozilla Firefox emerged as legitimate competitors to Microsoft’s Internet Explorer without regulatory remedy. In the end, Microsoft reached a settlement with the DOJ that fell far short of the government’s original ambitions to bust up the firm, all because the market moved at a pace much faster than the regulator’s pace. More recent antitrust action in the US and EU also suffer from the pacing problem. Multi-year antitrust investigations reach conclusions that don’t reflect market trends in the intervening years and offer remedies that may be “too little, too late,” especially in the information technology sector.

Is the Pacing Problem Really the Pacing Benefit?

What should policymakers do in light of these new challenges? The extremes will not work. Lawmakers or regulators cannot simply double-down on the lethargic and unwieldy technocratic regulatory schemes of the past. Command-and-control tactics are not going to be effective in an age when technology evolves in a quicksilver fashion. In a world where “innovation arbitrage” is easier than ever, repressive crackdowns on new tech will often backfire. Evasive entrepreneurs will often move to those jurisdictions where their innovative acts are treated more hospitably. That, too, exacerbates the pacing problem.

From the perspective of many innovation advocates, this will make it seem like the pacing problem is more like the pacing  benefit. Generally speaking, that intuition is sound. Innovation is the fundamental driver of human betterment. We need more “moonshots”—“radical but feasible solutions to important problems”—to ensure that current and future generations enjoy more choices, greater mobility, increased wealth, better health, and longer lifespans. We don’t want archaic regulatory schemes and regimes holding that back.

Constructive Solutions

But policymakers will not abandon oversight of emerging technologies altogether, nor should we want them to. The potential harms associated with some new technologies could be significant enough that a certain degree of regulatory oversight will be required. But the pacing problem means the old, inflexible, top-down approaches will need to be discarded and that the administrative state itself must become more entrepreneurial.

In a forthcoming law review article entitled, “Soft Law for Hard Problems: The Governance of Emerging Technologies in an Uncertain Future,” Jennifer Skees, Ryan Hagemann, and I discuss how “soft law” mechanisms—multi-stakeholder processes, industry best practices and standards, workshops, agency guidance, and more—can help fill the governance gap as the pacing problem accelerates. Many agencies are already tapping soft law tools to help guide the development of new technologies such as driverless cars, drones, the Internet of Things, mobile medical applications, artificial intelligence, and others. In fact, we argue that soft law has already become the dominant form of technological governance for emerging tech in the US.

Critics might decry soft law as either being too lax (and open to private abuse) or too informal (and open to government abuse), but the pacing problem makes both arguments increasingly irrelevant. We need a new governance vision for the technological age. Our new governance systems must be more flexible and adaptive than the heavy-handed regulatory regimes that preceded them.

___________________

Related Reading

]]> https://techliberation.com/2018/08/10/the-pacing-problem-and-the-future-of-technology-regulation/feed/ 0 76342 Governing Virtual Reality Social Spaces https://techliberation.com/2018/03/05/governing-virtual-reality-social-spaces/ https://techliberation.com/2018/03/05/governing-virtual-reality-social-spaces/#comments Mon, 05 Mar 2018 14:56:34 +0000 https://techliberation.com/?p=76241

“You don’t gank the noobs” my friend’s brother explained to me, growing angrier as he watched a high-level player repeatedly stalk and then cut down my feeble, low-level night elf cleric in the massively multiplayer online roleplaying game World of Warcraft. He logged on to the server to his “main,” a high-level gnome mage and went in search of my killer, carrying out two-dimensional justice. What he meant by his exclamation was that players have developed a social norm banning the “ganking” or killing of low-level “noobs” just starting out in the game. He reinforced that norm by punishing the overzealous player with premature annihilation.

Ganking noobs is an example of undesirable social behavior in a virtual space on par with cutting people off in traffic or budging people in line. Punishments for these behaviors take a variety of forms, from honking, to verbal confrontation, to virtual manslaughter. Virtual reality social spaces, defined as fully artificial digital environments, are the newest medium for social interaction. Increased agency and a sense of physical presence within a VR social world like VRChat allows users to more intensely experience both positive and negative situations, thus reopening the discussion for how best to govern these spaces.

When the late John Perry Barlow, the founder of the Electronic Frontier Foundation, published his declaration of the Independence of Cyberspace in 1996, humanity stood on the frontier of an online world bereft of physical borders and open to new emergent codes of conduct. He wrote, “I declare the global social space we are building to be naturally independent of the tyrannies [governments] seek to impose on us.” He also stressed the role of “culture, ethics and unwritten codes” in governing the new social society where the First Amendment served as the law of the virtual land. Yet, Barlow’s optimism about the capacity of users to build a better society online stands in stark contrast to current criticisms of social platforms as cesspools of misinformation, extremism, and other forms of undesirable behavior.

As the result of VRChat’s largely open-ended design and its wide user base from the PC and headset gaming communities, there is a broad spectrum of user behavior.  On one hand, users experienced virtual sexual harassment and the incessant trolling of mobs of poorly rendered “echidna” consistent with the Ugandan Knuckles meme. However, VRChat is also the source of creativity and positive experience including collective concerts and dance parties. When a player suffered a seizure in VRChat, players stopped and waited to make sure he was okay and sanctioned other players who were trying to make fun of the situation. VRChat’s response to social discord provides a good example of governance in virtual spaces and how layers of governance interact to improve user experiences.

Governance is the process of decision-making among stakeholders involved in a collective problem that leads to the production of social norms and institutions. In virtual social spaces such as VRChat, layers of formal and informal governance are setting the stage for norms of behavior to emerge. The work of political scientist Elinor Ostrom provides a framework through which to understand the evolution of rules to solve social problems. In her research on governing a common resource, she emphasized the importance of including multiple stakeholders in the governing process, instituting a mechanism for dispute resolution and sanctioning, and making sure the rules and norms that emerge are tailored to the community of users. She wrote, “building trust in one another and developing institutional rules that are well matched to the ecological systems being used are of central importance for solving social dilemmas.” Likewise, the governance structure that emerge in VRChat is game-specific and dependent on the enforcement of explicit formal and informal laws, physical game design characteristics, and social norms of users. I delve into each layer of governance in turn.

At the highest level, the U.S. government passed formal laws and policies that affect virtual social spaces. For example, the Computer Fraud and Abuse Act governs computer-related crimes and prohibits unauthorized access to user’s accounts. Certain types of content, such as child pornography, are illegal under federal law. At the intersection of VR video games and intellectual property law, publicity rights govern the permissions process for using celebrities’ likenesses in an avatar. Trademark and copyright laws determine limitations on what words, phrases, symbols, logos, videos or music can be reproduced in VR and what is considered “fair use.”

Game designers and gaming platforms can also employ an explicit code of conduct that goes beyond formal federal laws and policies. For example, VRChat’s code of conduct details proper Mic etiquette and includes rules about profanity, sexual conduct, self-promotion and discrimination. Social platforms rely on a team of enforcers. VRChat has a moderation team that monitors virtual worlds constantly. External reviewers look at flagged content and in-game bouncers monitor behavior in real time and remove the bad eggs.

By virtue of their technical decisions, game designers also govern the virtual spaces they create. For example, the design decision to put a knife or a banana in a VR social space will affect how users behave. VRChat has virtual presentation rooms, court rooms and stages that prompt users to do anything from singing, to stand-up comedy to prosecuting other users in fake trials. Furthermore, game designers can include in-game mechanisms to empower users to flag inappropriate behavior or mute obnoxious players, a function that exists in VRChat.

Earning a reputation for malfeasance and poor user experience is bad business for VRChat, so the company recently re-envisioned their governance approach. They acknowledge their task in an open letter to their users: “One of the biggest challenges with rapid growth is trying to maintain and shape a community that is fun and safe for everyone. We’re aware there’s a percentage of users that choose to engage in disrespectful or harmful behavior…we’re working on new systems to allow the community to better self-moderate and for our moderation team to be more effective.” The memo detailed where users could provide feedback and ideas to improve VRChat, suggesting that users can be actively involved in the rule-making process.

In Elinor Ostrom’s nobel-prize lecture she criticizes the oft-made assumption that enlightened policymakers or external designers should be the ones “to impose an optimal set of  rules on individuals involved.” Instead, she argued that the self-reflection and creativity of those users within a game could serve “to restructure their own patterns of interaction.” The resulting social norms are a form of governance at the most local level.

Ostrom’s framework demonstrates that good social outcomes emerge through our collective actions, which are influenced by top-down formal rules from platforms and bottom-up norms from users. The goal of stakeholders involved in social VR should be to foster the development of codes of conduct that bring out the best in humanity. Governance in virtual worlds is a process, and players in social spaces have a large role to play. Are you ready for that responsibility, player one?

]]> https://techliberation.com/2018/03/05/governing-virtual-reality-social-spaces/feed/ 4 76241 Are “Permissionless Innovation” and “Responsible Innovation” Compatible? https://techliberation.com/2017/07/12/are-permissionless-innovation-and-responsible-innovation-compatible/ https://techliberation.com/2017/07/12/are-permissionless-innovation-and-responsible-innovation-compatible/#respond Wed, 12 Jul 2017 18:28:55 +0000 https://techliberation.com/?p=76164

“Responsible research and innovation,” or “RRI,” has become a major theme in academic writing and conferences about the governance of emerging technologies. RRI might be considered just another variant of corporate social responsibility (CSR), and it indeed borrows from that heritage. What makes RRI unique, however, is that it is more squarely focused on mitigating the potential risks that could be associated with various technologies or technological processes. RRI is particularly concerned with “baking-in” certain values and design choices into the product lifecycle before new technologies are released into the wild.

In this essay, I want to consider how RRI lines up with the opposing technological governance regimes of “permissionless innovation” and the “precautionary principle.” More specifically, I want to address the question of whether “permissionless innovation” and “responsible innovation” are even compatible. While participating in recent university seminars and other tech policy events, I have encountered a certain degree of skepticism—and sometimes outright hostility—after suggesting that, properly understood, “permissionless innovation” and “responsible innovation” are not warring concepts and that RRI can co-exist peacefully with a legal regime that adopts permissionless innovation as its general tech policy default. Indeed, the application of RRI lessons and recommendations can strengthen the case for adopting a more “permissionless” approach to innovation policy in the United States and elsewhere.

Definitional Ambiguities, Part 1: “Governance”

Before we can have a constructive conversation about these issues, however, we need to agree upon how narrowly or broadly we are defining some relevant terms, beginning with the word “governance.” When some hear the term “governance” their first reaction might be to think “government,” and formal legal and regulatory processes in particular. That is certainly one form of governance, but it is hardly the only one.

We often speak of the “governance” of corporations, schools, churches, other institutions, and even households. When we do, we usually do not mean government administration of these things; we are instead thinking of some other, more amorphous form of governance by a variety of individuals or groups. The “governance” of a company, for example, includes the interaction of shareholders, board members, corporate officials, workers, and so on. The “governance” of a church might involve clergy, the congregation, and sacred scriptures or traditions.  Household “governance” comes down to decisions made by parents and caretakers. And so on.

Thus, “governance” can certainly have the narrow connotation of being associated with formal regulatory enactments by governments, but it can also describe a much broader universe of norms and rules that are established and enforced by a wide variety of people (or groups of people) in a wide variety of ways.

When we consider questions of technological governance—and specifically the notion of “anticipatory governance,” which is prominent feature of RRI discussions—it helps to specify whether we are speaking of governance in a broad or narrow sense. Whether it is done consciously or not, in much of the literature, RRI scholars and advocates fail to make it clear what type of “governance” they are thinking of when proposing new forms of anticipatory technological governance.

Definitional Ambiguities, Part 2: “Precautionary Principle” & “Permissionless Innovation”

These distinctions are particularly important when we compare and contrast the “precautionary principle” and “permissionless innovation.” These concepts are most useful when viewed as governance dispositions or policy postures and they are usually—although not always—used in the narrow “governance” sense to describe one’s perspective on where legal and regulatory defaults should be set.

Even when applied narrowly, however, both terms are open to interpretation as applied in various policy contexts. For example, precaution could mean an outright prohibition on an innovative activity until such time as it had been proven safe (this is the way many FDA or FAA regulations work). But precaution might be imposed through somewhat less restrictive approaches, such as a set of government-established safety standards buttressed by a recall regime (think NHTSA or CPSC). Even less restrictive but still precautionary in orientation would be a mandatory labeling law or a government-led risk reduction educational campaign. In other words, there are probably as many flavors of the precautionary principle as there are flavors of ice cream.

For the longest time, both proponents and critics of the precautionary principle have failed to put a name on its opposing worldview or governance disposition. I have argued that, despite its uncertain origin and imprecise meaning, “permissionless innovation” provides a useful name for the antithesis of the precautionary principle.

As I noted in a recent speech at an Arizona State University law school conference on technological governance, critics of permissionless innovation sometimes like to imply that it is synonymous with anarchy. (In fact, a few people at that event leveled that accusation at me.) But I’ve written an entire book on this notion and surveyed countless essays and articles that cite the term, and I have never once seen any advocate of permissionless innovation going to such an extreme. In fact, those advocates often don’t even bother calling for the abolition of any laws, programs, or agencies. As I noted in my ASU talk, “most of those defenders of permissionless innovation are using the term as a sort of shorthand when what they really mean to say is something like: ‘give innovators a bit more breathing room,’ or, ‘don’t rush to regulate.’”

And so, as a policy posture, permissionless innovation really comes down to a preference for setting public policy defaults closer to green lights rather than red ones. In my own book on the subject, I defined the term as follows:

“Permissionless innovation refers to the notion that experimentation with new technologies and business models should generally be permitted by default. Unless a compelling case can be made that a new invention will bring serious harm to society, innovation should be allowed to continue unabated and problems, if any develop, can be addressed later.”

By contrast, the precautionary principle posture generally recommends keeping the light red until innovators can prove their new products and services are “safe,” however that is defined. But there are many points along the spectrum between these two policy postures. And if we can accept the idea that the “precautionary principle” and “permissionless innovation” act more as general governance dispositions instead of fixed and rigid edicts, then it is also easier to imagine how both of those dispositions can incorporate “responsible innovation” notions into their governance visions.

Definitional Ambiguities, Part 3: “Responsible Innovation”

But what exactly constitutes “responsible innovation”? Definitions of responsible research and innovation are still evolving, but a leading article on the subject by René von Schomberg from 2011 argues that it can be defined as:

“A transparent, interactive process by which societal actors and innovators become mutually responsive to each other with a view to the (ethical) acceptability, sustainability and societal desirability of the innovation process and its marketable products (in order to allow a proper embedding of scientific and technological advances in our society).”

A more streamlined definition was offered by Jack Stigloe, Richard Owen, and Phil Macnaghten in a 2013 article: “Responsible innovation means taking care of the future through collective stewardship of science and innovation in the present.” They also proposed four dimensions of responsible innovation—anticipation, reflexivity, inclusion and responsiveness—which they say “provide a framework for raising, discussing and responding to such questions.”

RRI Tools, a European consortium focused on promoting responsible innovation strategies, identifies the six core goals of RRI as: open access, gender equality in science, ethics, science education, governance, and public engagement. Other groups and individuals promoting RRI focus on privacy, safety, and security as crucial values that they hope to work into more product development processes early on.

As with “corporate social responsibility” before it, “responsible innovation” will remain a term that is open to varying interpretations and which can incorporate many distinct values that are context-dependent. What Milton Friedman said of CSR discussions in 1970—that they “are notable for their analytical looseness and lack of rigor”—continues to be somewhat true for both CSR and RRI circa 2017. Nonetheless, what both concepts hold in common is the belief that, whatever those “responsible” values are, they can be “baked in” to corporate decision-making and product design processes in an anticipatory fashion.

And while not everyone will agree on the contours of these concepts, practically speaking, I think we can expect both the CSR and RRI movement will continue to grow in coming years. That will be the case not only because of the pressures applied by various activists, stakeholders, and governments, but also because many companies and their consumers will demand more than just better products and greater profitability.

But Doesn’t RRI Necessitate the Precautionary Principle as a Policy Prerequisite?

But how precisely should RRI notions and recommendations influence policy deliberations over the future course of technological governance in the narrow sense of the term (i.e., more legalistic sense)? Here’s where things get more interesting.

The problem is that many of the advocates of RRI are seemingly more sympathetic to precautionary policy regimes and skeptical of the wisdom of permissionless innovation as a policy default. This is not always well-articulated in their writing. Instead, it is the attitude seemingly on display when I speak with RRI advocates or hear them deliver speeches.  Yet, most of these advocates just won’t ever let you nail them down on the point.

Some RRI advocates do come close to making that connection. In his seminal article, Rene von Schomberg argues that RRI, “can reduce the human cost of trial and error and make advantage of a societal learning process of stakeholders and technical innovators. It creates a possibility for anticipatory governance,” he says. “This should ultimately lead to products which are (more) societal robust.”

He then briefly raises the possibility of RRI informing the application of the precautionary principle in public policy debates:

“The precautionary principle works as an incentive to make safe and sustainable products and allow governmental bodies to intervene with Risk Management decisions (such as temporary licensing, case by case decision making etc) whenever necessary in order to avoid negative impacts.”

Yet, von Schomberg never really spells out the exact relationship between RRI and the precautionary principle as a matter of public policy .

Another leading article on the meaning of RRI by Grace Eden, Marina Jirotka, and Bernd Stahl, says that, “The RRI focus is more on mitigating wider societal long-term risks and so favors incremental rather than radical innovation.” That seems to suggest a closer connection between RRI and a formal application of the precautionary principle in policy deliberations about emerging technologies. They also speak of the “two very different approaches to problem solving (anticipatory vs. evidence-based),” which I have argued gets to the heart of the divergence between the precautionary principle and permissionless innovation policy paradigms. Yet, these authors do not dwell on this connection at length, and most of the rest of their article is focused on the ways in which RRI can (and already does) infuse product and service development processes outside of the realm of public policy.

In a 2015 Brookings Institution white paper about RRI, Walter D. Valdivia and David H. Guston offer a more concrete answer to this question when they insist that responsible innovation “is not a doctrine of regulation and much less an instantiation of the precautionary principle; the actions it recommends do not seek to slow down innovation because they do not constrain the set of options for researchers and businesses, they expand it.” They continue on to note that:

“[responsible innovation] considers innovation inherent to democratic life and recognizes the role of innovation in the social order and prosperity. It also recognizes that at any point in time, innovation and society can evolve down several paths and the path forward is to some extent open to collective choice. What RI pursues is a governance of innovation where that choice is more consonant with democratic principles.”

Here, finally, we have a better demarcation between the general notion of RRI and the formal application of the precautionary principle. But is that line really so bright? Do other RRI scholars agree with Valdivia and Guston about this separation between the “responsible innovation” movement and the formal application of the precautionary principle in the policy realm? And, finally, what is meant by “democratic life” and “democratic principles” in this context?

I suspect that many RRI advocates would read that last line from Valdivia and Guston above (“What RI pursues is a governance of innovation where that choice is more consonant with democratic principles.”) and suggest that it favors an embrace of the precautionary principle as the default position in emerging technology policy discussions. But, again, that remains open to debate because so much of the RRI literature lacks precision regarding the connection between these concepts.

How RRI Can be Compatible with Both Visions

Regardless, I would like to suggest that parties on both sides of this debate would be wise to divorce the concept of responsible innovation from their priors regarding optimal regulatory policy toward emerging technology. Properly understood, “responsible innovation” could be a feature of the “precautionary” vision, but it could also be compatible with the “permissionless” governance vision and resulting policy regimes. To reach that understanding, both sides will need to be open to learning from the other and willing to take their concerns seriously.

Advocates of RRI should understand that, just as CSR can do a great deal of good even in the absence of formal regulatory action, the same can be true of RRI, even in a policy regime in which permissionless innovation is the general default.

If, however, the first instinct among the RRI community is to consider advocates of permissionless innovation nothing more than a bunch of uncaring anarchists, they relinquish the opportunity to work with diverse parties to instill wise guidelines into technological development processes. This would be particularly misguided in an age when the so-called “Pacing Problem”—i.e., the growing gap between the introduction of new technologies and time it takes laws and regulations to adjust or be formulated in response—has become an ever-accelerating reality, making traditional “hard law” regulatory enactment increasingly difficult. If the RRI community wants to get any of the values that they care about incorporated into technological development processes, then they will need to be open to the idea that perhaps the only way to do so will be through less formal procedures precisely because law will likely lag so far behind marketplace developments.

Likewise, if the first instinct among the permissionless innovation advocates is to regard the RRI movement as little more than repackaged Ludditism, hell-bent on derailing all the great inventions of the future, then they are foolishly forgoing the chance to work with a diverse group of well-intentioned scholars and stakeholders who could ensure that new products and services gain more widespread acceptance and public trust. More practically, permissionless innovation advocates would be wise to accept the fact that, although technological innovation is generally outpacing the ability of government to keep up, that doesn’t mean most of the traditional regulatory regimes or agencies are going away any time soon. After all, can you name a technocratic law or regulatory body that has been liberalized or eliminated in recent memory? RRI offers a chance to forge a rough peace with agencies and officials who often just want to have a small say in how innovative processes are unfolding. Of course, if regulators seek to have a BIG say in those matters, then policy fights will no doubt ensue. But in my experience, this is less often the case than some defenders of permissionless innovation suggest.

Thus, advocates of permissionless innovation should understand that RRI is not synonymous with a formal precautionary principle-focused policy prescription and that “anticipatory governance” can mean something more generic and beneficial, so long as it does not come to mean the formal application of the precautionary principle as the public policy default.

We Are Already Going Down This Path

Perhaps I am being naïve to think this sort of common ground might exist. But the funny thing is that I know for a fact that it already does! RRI principles have been infusing various multistakeholder processes in the United States for many years now.

For example, here’s a paper I wrote back in 2009 about the various online safety task forces, blue ribbon commissions, and other collaborative efforts that were instilling “safety by design” principles into various online services and digital products. Meanwhile, “privacy by design” and “security by design” efforts are all the rage these days and a wide variety of best practices and codes of conduct have been established to make sure privacy and security values are baked-in to the product design process from the start.

Meanwhile, safety, security, and privacy best practices have increasingly been formulated by the U.S. Department of Commerce (the National Telecommunications and Information Administration in particular), the Federal Trade Commission, FDA, FCC, and the White House Office of Science and Technology Policy. These multistakeholder efforts and agency best practice reports have contained assorted “responsible innovation” principles for technologies as wide-ranging as: big data, artificial intelligence, the Internet of Things, facial recognition, online advertising, mobile phone privacy, mobile apps for kids, driverless cars, commercial drones, genetic testing, medical advertising on social media, 3D printed medical devices, medical device cybersecurity, nanotech, and much more. (I have a forthcoming paper in the works with Ryan Hagemann of the Niskanen Center in which we attempt to document many of these new “soft law” technological governance efforts. There have been so many of these efforts – many of which are still underway – that we are having a hard time cataloging them all!)

I am utterly perplexed why more RRI scholarship has not identified the many ways in which the principles they advocate already infuse multistakeholder processes such as these. Perhaps it is because those scholars feel that some of these multistakeholder processes fail to address the full range of issues or values that they feel are in play. But if you examine recent reports from these agencies and government bodies, I think you will come away quite impressed by the breadth of issues and concerns that they cover. Likewise, the values and best practices they discuss and/or recommend are exactly the sort of responsible innovation principles that the RRI movement cares about.

To some extent, therefore, RRI is already well-entrenched in the technology governance process, it’s just a bit messy. I think some RRI scholars probably fall prey to the old “Goldilocks myth” that we can get these principles just right with enough consideration and oversight. The reality on the ground is that instilling RRI values into the technological design process is a dynamic, iterative, and quite imprecise art.

In closing, there’s still more to the technological governance story that RRI advocates fail to incorporate into their work. To fully appreciate the many ways technological processes are constrained and corrected, they must take into account other governance forces and factors, including the role of:

  • social norms and reputational effects (especially the growing importance of reputational feedback mechanisms);
  • third-party accreditation and standards-setting bodies;
  • courts and common law (including legal solutions like product liability, negligence, design defects law, failure to warn, breach of warranty, and other assorted torts and class action claims);
  • insurance markets as risk calibrators and correctional mechanisms;
  • federal and state consumer protection agencies (such as the FTC), which police “unfair and deceptive practices” and other harms; and
  • media, academic institutions, non-profit advocacy groups, and the general public more generally, all of which can put pressure on technology developers.

Only by taking into account the full range of players and activities at work can we develop a more robust understanding of how technology is actually “governed” in our modern world. I suspect that many in the RRI community of scholars do appreciate these other factors, even though they don’t always account for all of them in their writing and advocacy. Then again, many of those advocates would perhaps decry the more remedial, ex post nature of these governance tools and insist that more ex ante anticipatory planning must be at the heart of technological design and development processes.

In reality, a mix of these two approaches is already at work today and will likely continue to dominate the governance process well into the future. So long as the anticipatory efforts don’t become formal regulatory proposals, there is no reason that this mix of “responsible innovation” governance tools and methods can’t be embraced by a diverse array of scholars and innovators.

Further Reading:

]]> https://techliberation.com/2017/07/12/are-permissionless-innovation-and-responsible-innovation-compatible/feed/ 0 76164 Does “Permissionless Innovation” Even Mean Anything? https://techliberation.com/2017/05/18/does-permissionless-innovation-even-mean-anything/ https://techliberation.com/2017/05/18/does-permissionless-innovation-even-mean-anything/#comments Thu, 18 May 2017 22:49:28 +0000 https://techliberation.com/?p=76143

[Remarks p repared for Fifth Annual Conference on Governance of Emerging Technologies: Law, Policy & Ethics at Arizona State University, Phoenix, AZ, May 18, 2017.]

_________________

What are we to make of this peculiar new term “permissionless innovation,” which has gained increasing currency in modern technology policy discussions? And how much relevance has this notion had—or should it have—on those conversations about the governance of emerging technologies? That’s what I’d like to discuss here today.

Uncertain Origins, Unclear Definitions

I should begin by noting that while I have written a book with the term in the title, I take no credit for coining the phrase “permissionless innovation,” nor have I been able to determine who the first person was to use the term. The phrase is sometimes attributed to Grace M. Hopper, a computer scientist who was a rear admiral in the United States Navy. She once famously noted that, “It’s easier to ask forgiveness than it is to get permission.”

“Hopper’s Law,” as it has come to be known in engineering circles, is probably the most concise articulation of the general notion of “permissionless innovation” that I’ve ever heard, but Hopper does not appear to have ever used the actual phrase anywhere. Moreover, Hopper was not necessarily applying this notion to the realm of technological governance, but was seemingly speaking more generically about the benefit of trying new things without asking for the blessing of any number of unnamed authorities or overseers—which could include businesses, bosses, teachers, or perhaps even government officials.

Today, however, we most often hear the “permissionless innovation” used in discussions about the governance of information technologies as well as a wide variety of emerging technologies. Unfortunately, scholars and advocates who have suggested that permissionless innovation should serve as the governing lodestar in these areas do not always precisely define what they mean by the term.

None of them seem to be suggesting, however, that permissionless innovation is synonymous with anarchy. To the contrary, many of them are quick to note that governments will continue to have a role to play. It is even rare to see advocates of permissionless innovation in these varied contexts calling for the abolition of any laws, programs, or agencies.

Instead, it seems to be the case that most of those defenders of permissionless innovation are using the term as a sort of shorthand when what they really mean to say is something like: “give innovators a bit more breathing room,” or, “don’t rush to regulate.”

This is consistent with my own articulation of the term, which goes as follows:

“Permissionless innovation refers to the notion that experimentation with new technologies and business models should generally be permitted by default. Unless a compelling case can be made that a new invention will bring serious harm to society, innovation should be allowed to continue unabated and problems, if any develop, can be addressed later.”

Default Policy Positions

Framing the term in this fashion makes it clear that, as it pertains to technological governance, permissionless innovation is about setting our public policy defaults closer to green lights rather than red ones.

It switches the burden of proof to the opponents of ongoing technological change by asserting five things:

  • First, technological innovation is the single most important determinant of long-term human well-being.
  • Second, there is real value to learning through continued trial-and-error experimentation, resiliency, and ongoing adaptation to technological change.
  • Third, constraints on new innovation should be the last resort, not the first. Innovation should be innocent until proven guilty.
  • Fourth, as regulatory interventions are considered, policy should be based on evidence of concrete potential harm and not fear of worst-case hypotheticals.
  • Fifth, and finally, where policy interventions are deemed needed, flexible, bottom-up solutions of an ex post (responsive) nature are almost always preferable to rigid, top-down controls of an ex ante (anticipatory) nature.

Shared Shortcomings of Both Visions

At least on the surface, that sort of governance vision stands in stark contrast to the “precautionary principle.” Defenders of the precautionary principle as the general default position in technology policy debates generally believe that new innovations should be curtailed or disallowed until their developers can prove that they will not cause any harm to individuals, groups, specific entities, cultural norms, or various existing laws, norms, or traditions.

That being said, I’d like to point out some of the shared shortcomings of both of these governance visions.

First, as with attempts to define the parameters of “permissionless innovation,” the precautionary principle is not always as rigid as its critics sometimes suggest. There are as many flavors of the precautionary principle as there are ice cream. Indeed, this is why many have criticized the precautionary principle not for what it says but rather for what it doesn’t say. It doesn’t tell us exactly how and when to apply precautionary measures, or how to evaluate the trade-offs associated with precaution.

This points the second and deeper underlying problem faced by advocates of both precautionary measures and permissionless innovation: Our collective inability to craft a widely-shared definition of what constitutes “technological harm” in various contexts. This is certainly not to suggest that no attempt has been made to do so. Rather, simply that we don’t seem to be any closer to concrete agreement about how or where to draw those lines.

Of course, let’s not kid ourselves into thinking that we can find bright-line answers to all these questions. After all, for many of these technological governance issues we are operating in the realm of “Level 3” or “Earth-level” systems, as Professors Allenby and Sarewitz refer to it in their book, The Techno-Human Condition. These are systems in which we deal with, as they say, “a context that is always shifting, and on meanings that are never fixed.”

That makes it even more challenging to define what we mean by “responsible innovation” or “socially desirable innovation” for purposes of determining optimal technology policy.

Risk Analysis through the Lens of Permissionless Innovation

For me, there are no easy ways out of this mess. But I do know two things for certain.

First, we must continue to refine and improve our risk analysis tools and techniques to make better determinations of when proposed interventions are sensible and cost-effective relative to the many trade-offs at work.

Again, I recognize the challenge of doing this when many of the issues and values in play are amorphous and metaphysical conflicts exist about how to even define some of these things. Most of the emerging technology policy issues I write about today, for example, involve some sort of privacy, safety, or security concern. In each case, however, very little consensus exists about what those terms even mean in varied contexts.

Nonetheless, the fact that benefit-cost analysis is hard should not serve as an excuse for failing to go through the exercise of attempting some sort of valuation of the many variables in play.

Soft Law Alternatives

The second thing I know for certain is that, due the combination of both definitional complexity regarding what constitutes technological harm, as well as the ever-accelerating pace of the so-called “pacing problem,” all roads lead back to soft law solutions instead of hard law remedies.

Last year, I had the pleasure of reading and reviewing Wendell Wallach’s new book and then having a nice conversation with him about it at Microsoft’s DC headquarters. The most interesting thing about our exchange was that, although we do not begin in the same place philosophically-speaking, we largely end up in the same place practically-speaking.

That is, there seemed to be some grudging acceptance on both our parts that “soft law” systems, multistakeholder processes, and various other informal governance mechanisms will need to fill the governance gap left by the gradual erosion of hard law.

Many other scholars, including many of you in this room, have discussed the growth of soft law mechanisms in specific contexts, but I believe we have probably failed to acknowledge the extent to which these informal governance models have already become the dominant form of technological governance, at least in the United States.

I’m currently co-authoring a very long study which documents how the Obama Administration came to rely quite heavily on multistakeholder processes, negotiated “best practices,” and industry codes of conduct as the primary governance mechanisms for a long list of emerging tech issues, including: driverless cars, commercial drones, big data, facial recognition, the Internet of Things and wearable technology, mobile medical applications, 3D printing, artificial intelligence, the Sharing Economy, and much more.

Most of these soft law processes were driven by the NTIA and FTC, but plenty of other agencies with an “N” or an “F” at the beginning of their name have undertaken some sort of soft law process, including NHTSA, the FDA, the FAA, and so on.

Now, I’m willing to bet that many of those involved in these processes who generally favor more anticipatory regulatory approaches would have preferred to start with hard law solutions to some of these issues. And I am equally certain that many of the innovators involved in those multistakeholder processes would have probably preferred not to have had to come to the table at all.

But at the end of the day, for the most part, all sides did come to the table and worked together in a good faith effort to find some rough consensus about what sort of informal guidelines would govern the future of innovation in these sectors.

The Worst of All Systems, Except All the Others

Plenty of questions remain about such soft law systems, and the irony is that defenders of both permissionless innovation and the precautionary principle will quite often be raising very similar concerns regarding the transparency, accountability, and enforceability of these systems.

But I’m inclined to believe that no matter where you sit on the permissionless vs. precautionary spectrum, and no matter what your reservations may be about it the new world of soft law governance that we find ourselves moving into, this is the future and the future is now.

Much as Churchill said of democracy being “the worst form of Government except for all those other forms that have been tried from time to time,” I think we are well on our way to a world in which soft law is the worst form of technological governance except for all those others that have been tried before.

Of course, the devil is always in the details and I suspect that we’ll have plenty of discuss and debate in that regard. Let’s get that conversation going.

]]> https://techliberation.com/2017/05/18/does-permissionless-innovation-even-mean-anything/feed/ 5 76143 Innovation Arbitrage, Technological Civil Disobedience & Spontaneous Deregulation https://techliberation.com/2016/12/05/innovation-arbitrage-technological-civil-disobedience-spontaneous-deregulation/ https://techliberation.com/2016/12/05/innovation-arbitrage-technological-civil-disobedience-spontaneous-deregulation/#comments Mon, 05 Dec 2016 20:06:53 +0000 https://techliberation.com/?p=76096

The future of emerging technology policy will be influenced increasingly by the interplay of three interrelated trends: “innovation arbitrage,” “technological civil disobedience,” and “spontaneous private deregulation.” Those terms can be briefly defined as follows:

  • “Innovation arbitrage” refers to the idea that innovators can, and will with increasingly regularity, move to those jurisdictions that provide a legal and regulatory environment more hospitable to entrepreneurial activity. Just as capital now fluidly moves around the globe seeking out more friendly regulatory treatment, the same is increasingly true for innovations. And this will also play out domestically as innovators seek to play state and local governments off each other in search of some sort of competitive advantage.
  • “Technological civil disobedience” represents the refusal of innovators (individuals, groups, or even corporations) or consumers to obey technology-specific laws or regulations because they find them offensive, confusing, time-consuming, expensive, or perhaps just annoying and irrelevant. New technological devices and platforms are making it easier than ever for the public to openly defy (or perhaps just ignore) rules that limit their freedom to create or use modern technologies.
  • “Spontaneous private deregulation” can be thought of as de facto rather than the de jure elimination of traditional laws and regulations owing to a combination of rapid technological change as well the potential threat of innovation arbitrage and technological civil disobedience. In other words, many laws and regulations aren’t being formally removed from the books, but they are being made largely irrelevant by some combination of those factors. “Benign or otherwise, spontaneous deregulation is happening increasingly rapidly and in ever more industries,” noted Benjamin Edelman and Damien Geradin in a Harvard Business Review article on the phenomenon.[1]

I have previously documented examples of these trends in action for technology sectors as varied as drones, driverless cars, genetic testing, Bitcoin, and the sharing economy. (For example, on the theme of global innovation arbitrage, see all these various essays. And on the growth of technological civil disobedience, see, “DOT’s Driverless Cars Guidance: Will ‘Agency Threats’ Rule the Future?” and “Quick Thoughts on FAA’s Proposed Drone Registration System.” I also discuss some of these issues in the second edition of my Permissionless Innovation book.)

In this essay, I want to briefly highlight how, over the course of just the past month, a single company has offered us a powerful example of how both global innovation arbitrage and technological civil disobedience— or at least the threat thereof—might become a more prevalent feature of discussions about the governance of emerging technologies. And, in the process, that could lead to at least the partial spontaneous deregulation of certain sectors or technologies. Finally, I will discuss how this might affect technological governance more generally and accelerate the movement toward so-called “soft law” governance mechanisms as an alternative to traditional regulatory approaches.

Comma.ai Case Study, Part 1: The Innovation Arbitrage Threat

The company I want to highlight is Comma.ai, a start-up that had hoped to sell a $999 after-market kit for vehicles called the “Comma One,” which “would give average, everyday cars autonomous functionality.”[2] Created by famed hacker George Hotz, who as a teenager gained notoriety for being the first person to unlock an iPhone in 2007, the Comma One represents an attempt to create autonomous vehicle tech “on the cheap” by using off-the-shelf cameras and GPS technology combined with a healthy dose of artificial intelligence technology.

comma-one

But regulators at the National Highway Traffic Safety Administration (NHTSA), the federal agency responsible for road safety and automobile regulation, were none too happy to hear about Hotz’s plan to unleash his technology into the wild without first getting their blessing. On October 27, the agency fired off a nastygram to Hotz saying: “We are concerned that your product would put the safety of your customers and other road users at risk. We strongly encourage you to delay selling or deploying your product on the public roadways unless and until you can ensure it is safe.”

Hotz responded on Twitter promptly and angrily. After posting the full NHTSA letter, he said, “First time I hear from them and they open with threats. No attempt at a dialog.” In a follow-up tweet, he said, “Would much rather spend my life building amazing tech than dealing with regulators and lawyers. It isn’t worth it.” And then he announced that, “The comma one is cancelled. comma.ai will be exploring other products and markets. Hello from Shenzhen, China.” A flood of news articles followed about Hotz’s threat to engage in this sort of global innovation arbitrage by bolting US shores.[3]

Incidentally, what Hotz and Comma.ai were proposing to do with Comma One—i.e., deploy autonomous vehicle tech into the wild without prior regulatory approval—was recently done by Otto, a developer of autonomous trucking technology. As Mark Harris reported on Backchannel:

When Otto performed its test drive — the one shown in the May video — it did so despite a clear warning from Nevada’s Department of Motor Vehicles (DMV) that it would be violating the state’s autonomous vehicle regulations. When the DMV realized that Otto had gone ahead anyway, one official called the drive “illegal” and even threatened to shut down the agency’s autonomous vehicle program.”[4]

While Nevada regulators were busy firing off angry letters, Otto was busy doing even more testing in others states (like Ohio), which are eager to make their jurisdictions a testbed for autonomous vehicle innovation.[5] In fact, just recently, Ohio Gov. John Kasich announced the creation of the “Smart Mobility Corridor,” which, according to the Dayton Daily News, will be “a 35-mile stretch of U.S. 33 in central Ohio that runs through Logan County. Officials say that section of U.S. 33 will become a corridor where technologies can be safely tested in real-life traffic, aided by a fiber-optic cable network and sensor systems slated for installation next year.”[6]

otto-truck

This is an example of innovation arbitrage will increasingly take root here domestically as well as abroad, and some states (or countries) will use inducements in an effort to lure innovators to their jurisdictions.

Anyway, let’s get back to the Comma One case study. I don’t want to get too sidetracked regarding the merits of the concerns raised by NHTSA in its letter to Hotz and the implications of the agency’s threats for innovation in this space. But EFF board member Brad Templeton did a nice job addressing that issue in an essay about NHTSA’s letter that threatened Comma. As Templeton observed:

I will presume the regulators will say, “We only want to scare away dangerous innovation” but the hard truth is that is a very difficult thing to judge. All innovation in this space is going to be a bit dangerous. It’s all there trying to take the car — the 2nd most dangerous legal consumer product — and make it safer, but it starts from a place of danger. We are not going to get to safety without taking risks along the way.[7]

This gets to the very real trade-offs in play in the debate over driverless car technology and its regulation. In fact, my Mercatus Center colleague Caleb Watney and I recently filed comments [8] with NHTSA addressing the agency’s recently proposed “Federal Automated Vehicles Policy.”[9] We stressed the potentially deleterious implications of prior regulatory restraints on autonomous vehicle innovation by stressing the horrific real-world baseline we live with today, in which over 35,000 people dying on US roadways in 2015 (roughly 96 people per day) and 94 percent of all those crashes being attributable to human error.

Caleb and I noted that, by imposing new preemptive constraints on the coding of superior autonomous driving technology, “NHTSA’s proposed policy for automated vehicles may inadvertently increase the number of total automobile fatalities by delaying the rapid development and diffusion of this life-saving technology.” Needless to say, if that comes to pass, it would be a disaster because “automation on the roads could be the great public-health achievement of the 21st century.”[10]

In our filing, Caleb and I estimated that, “If NHTSA’s proposed premarket approval process slows the deployment of HAVs by 5 percent, we project an additional 15,500 fatalities over the course of the next 31 years. At 10 percent regulatory delay, we project an additional 34,600 fatalities over 33 years. And at 25 percent regulatory delay, we project an additional 112,400 fatalities over 40 years.[11]

So, needless to say, this is a very big deal.

But let’s ignore all those potential foregone benefits for the moment and just stick with the question of whether Hotz’s threat to engage in a bit of global innovation arbitrage (by moving to China or somewhere else) could work, or at least affect policy in some fashion. I think it absolutely could be an effective threat both because (a) policymakers really do want to do everything they can to achieve greater road safety, and (b) the auto sector remains a hugely important industry for the United States, and one that policymakers will want to do everything in their power to retain on our shores.

Moreover, as Templeton observes that “Comma is not the only company trying to build a system with pure neural networks doing the actual steering decisions.” Even if NHTSA succeeds in bringing Comma to heel, there will be others who will follow in its footsteps. It might be a firm like Otto, but there are many other players in this space today, including big dogs like Tesla and Google. If ever there was a truly global technology industry, it the automotive sector. Autonomous vehicle innovation could take root and blossom in almost any country in the world, and many countries will be waiting with open arms if America screws up its regulatory process.

As Templeton concludes:

The USA and California led the way in robocars in part because it was unregulated. In the USA, everything is permitted unless it was explicitly forbidden and nobody thought to write “no robots” in the laws. Progress in other countries where everything is forbidden unless it is permitted was much slower. The USA is moving in the wrong direction.[12]

Comma.ai Case Study, Part 2: The Technological Civil Disobedience Threat

But an interesting thing happened on the way to Comma’s threatened exodus. On November 30, the firm announced that it would now be open sourcing the code for its autonomous vehicle technology. Reporters at The Verge noted that, during a press conference:

Hotz said that Comma.ai decided to go open source in an effort to sidestep NHTSA as well as the California DMV, the latter of which he said showed up to his house on three separate occasions. “NHTSA only regulates physical products that are sold,” Hotz said. “They do not regulate open source software, which is a whole lot more like speech.” He went on to say that “if the US government doesn’t like this [project], I’m sure there are plenty of countries that will.”[13]

So here we see Hotz combining the threat of still potentially taking the project offshore (i.e., global innovation arbitrage) with the suggestion that by open-sourcing the code for Comma One he might be able to get around the law altogether. We might consider that an indirect form of technological civil disobedience.

george-hotz

Incidentally, Hotz may not be aware of the fact that NHTSA is in the process of making a power-play to become a driverless car code cop. While Hotz is technically correct that, under current law, NHTSA officials “do not regulate open source software, which is a whole lot more like speech,” NHTSA’s recent Federal Automated Vehicles Policy claimed that the agency “has authority to regulate the safety of software changes provided by manufacturers after a vehicle’s first sale to a consumer” while also suggesting that the agency “may need to develop additional regulatory tools and rules to regulate the certification and compliance verification of such post-sale software updates.”[14]

Needless to say, this proposal has important ramifications for not only Comma, but all other firms in this sector. Consider the implications for Tesla’s “autopilot” mode, which is really little more than a string of constantly-evolving code it pushes out to offer greater and greater autonomous driving functionality.  How would that iterative process work if every time Tesla wanted to make a little tweak to its code it had to run to Washington and file paperwork with NHTSA petitioning for permission to experiment and improve their systems? And then think about all the smaller innovators out there who want to be the next Elon Musk or George Hotz but do not yet have the resources or political connections in Washington to even go through this complex and costly process.

In any event, I have no idea if Hotz or Comma.ai will follow through with any of these threats or be successful in doing so. It may be the case that he is just blowing off smoke and that he and his firm will end up staying in the U.S. and perhaps even later reversing course on the decision to open source the Comma code. But to the extent that innovators like Hotz even hint that they might split the country or open source their code to avoid burdensome regulatory regimes, it can have an influence on future policy decisions. Or at least it should.

New Tech Realities & Their Policy Implications

Indeed, the increasing prevalence of global innovation arbitrage and technological civil disobedience raise some interesting issues for the governance of emerging technologies going forward. The traditional regulatory stance toward many existing sectors and technologies will be challenged by these realities. That’s because most of those traditional regulatory systems are highly precautionary, preemptive, and prophylactic in character. They generally opt for policy solutions that are top-down, overly rigid, and bureaucratic.

marcandreessen
This results in a slow-moving and sometimes completely stagnant regulatory approval process that can stop innovation dead in its tracks, or at least delay it for many years. Such systems send innovators a clear message: You are guilty until proven innocent and must receive some bureaucrat’s blessing before you can move forward.

Of course, in the past, many innovators (especially smaller scale entrepreneurs) really couldn’t do much to avoid similar regulatory systems where they existed. You either fell into line, or else! It wasn’t always clear what “or else!” would entail, but it could range from being denied a permit/license to operate, waiting months or years for rules to emerge, dealing with fines or other penalties, or some combination of all those things. Or perhaps you would just give up on your innovative idea altogether and exit the market.

But the world has changed in some important ways in recent years. Many of the underlying drivers of the digital revolution—massive increases in processing power, exploding storage capacity, steady miniaturization of computing, ubiquitous communications and networking capabilities, the digitization of all data, and more—are beginning to have a profound impact beyond the confines of cyberspace.[15] As venture capitalist Marc Andreessen explained in a widely read 2011 essay about how “software is eating the world”:

More and more major businesses and industries are being run on software and delivered as online services—from movies to agriculture to national defense. Many of the winners are Silicon Valley-style entrepreneurial technology companies that are invading and overturning established industry structures. Over the next 10 years, I expect many more industries to be disrupted by software, with new world-beating Silicon Valley companies doing the disruption in more cases than not. Why is this happening now? Six decades into the computer revolution, four decades since the invention of the microprocessor, and two decades into the rise of the modern Internet, all of the technology required to transform industries through software finally works and can be widely delivered at global scale.[16]

We can add to this list of a new realities the more general problem of technology accelerating at an unprecedented pace. This is what philosophers of technology call the “pacing problem.”  In his new book,  A Dangerous Master: How to Keep Technology from Slipping beyond Our Control, Wendell Wallach concisely defined the pacing problem as “the gap between the introduction of a new technology and the establishment of laws, regulations, and oversight mechanisms for shaping its safe development.” “There has always been a pacing problem,” Wallach correctly observed, but like other philosophers, he believes that modern technological innovation is accelerating much faster than it was in the past.[17]

What are the ramifications of all this for policy? As technology lawyer and consultant Larry Downes has noted, lawmaking in the information age is now inexorably governed by the “law of disruption” or the fact that “technology changes exponentially, but social, economic, and legal systems change incrementally.”[18] This law is “a simple but unavoidable principle of modern life,” he said, and it will have profound implications for the way businesses, government, and culture evolve. “As the gap between the old world and the new gets wider,” he argues, “conflicts between social, economic, political, and legal systems” will intensify and “nothing can stop the chaos that will follow.”[19]

laws-of-disruption

The end result of the “law or disruption” and a world relentlessly governed by the ever-accelerating “pacing problem” is that it will be harder than ever to effectively control emerging technologies using traditional legal and regulatory systems and mechanisms. And this makes it even more likely that the related threats of global innovation arbitrage and various forms of technological civil disobedience will become more regular fixtures in debates about many emerging technologies.

New Governance Models

How one reacts to these new realities will depend upon their philosophical disposition toward innovative activities more generally.

Consider first those adhering to a more “precautionary principle” mindset, which I have defined in my recent book as those who believe “that new innovations should be curtailed or disallowed until their developers can prove that they will not cause any harm to individuals, groups, specific entities, cultural norms, or various existing laws, norms, or traditions.”[20]

Needless to say, the precautionary principle crowd with be dismayed by these new trends and perhaps even decry them as “lawlessness.” Some of these folks seem to be in denial about these new realities and pretend that nothing much has changed. Yet, I have found that most precautionary principle-oriented advocates, and even many regulatory agencies themselves, tend to acknowledge these new realities. But they remain very uncertain about how best to respond to them, often just suggesting that we’ll all need to just try harder to impose new and better regulations on a more expedited or streamlined basis.

Of course, those of us who generally embrace the alternative policy vision for technological governance—“permissionless innovation”—are going to be more accepting of the new technological realities I have described, and we will perhaps even work to defend and encourage them. But while I count myself among this crowd, we cannot ignore the fact that many serious challenges will arise when innovation outpaces law or can easily evade it.

There is some middle ground here, although it is very messy middle ground.

The era of technocratic, top-down, one-size-fits-all regulatory regimes is fading, or at least being severely strained. We will instead need to craft flexible and adaptive policies going forward that are bottom-up, flexible, and evolutionary in character.

What that means in practice is that a lot more “soft law” and informal governance mechanisms will become the new norm. I wrote about this new policy environment in my recent essay, “DOT’s Driverless Cars Guidance: Will ‘Agency Threats’ Rule the Future?” as well as this lengthy review of Wendell Wallach’s latest book about technology ethics.  Along with Gary Marchant of the Arizona State University law school, Wallach recently published an excellent book chapter on “Governing the Governance of Emerging Technologies,” which discussed these soft law mechanisms, which include: “codes of conduct, statements of principles, partnership programs, voluntary programs and standards, certifications programs and private industry initiatives.”[21]

Their chapter appears in an important collection of essays that Gary Marchant edited with Kenneth W. Abbott and Braden Allenby entitled, Innovative Governance Models for Emerging Technologies.

governance-book

What is interesting about the chapters in that book is that seemingly widespread consensus now exists among experts in this field that some combination of these soft law mechanisms are likely to become the primary mode of technological governance for the indefinite future.  This is because, as Marc A. Saner points out in a different chapter of that book, “the control paradigm is too limited to address all the issues that arise in the context of emerging technologies.”[22] By the control paradigm, he generally means traditional administrative regulatory agencies and processes. He and other contributors in the book all seem to agree that the control problem paradigm “has its limits when diffusion, pacing and ethical issues associated with emerging technologies become significant, as is often the case.”[23]

And so the traditional command-and-control ways will gradually give way to a new paradigm for emerging technology governance. In fact, as I noted in my recent essay on driverless cars, we see this happening quite a bit already. “Multistakeholder processes” are already all the rage in the world of emerging technologies and their governance. In recent years, we have seen the White House and various agencies (such as the FTC, NTIA, FDA, and others) craft multistakeholder agreements or best practice guidance documents for technologies as far ranging as:

  • Drones & privacy
  • Sharing economy
  • Internet of Things
  • Driverless cars
  • Big data
  • Artificial intelligence
  • Cross-device tracking
  • Native advertising
  • Online data collection
  • Mobile app transparency and security
  • Mobile apps for kids
  • Mobile medical apps
  • Online health advertising
  • 3D printing
  • Facial recognition

And that list is not comprehensive. I know I am missing other multistakeholder efforts, best practices, or industry guidance documents that have been crafted in recent years.

Of course, many challenging issues need to be sorted out here, most notably: how transparent and accountable will these soft law systems be in practice? How will they be enforced? And what will happen to all those existing laws, regs, and agencies that will continue to exist? More generally, it is worth asking whether we can more closely study these various multistakeholder arrangements and soft law governance mechanisms and determine if there are certain principles or strategies that could be applicable across a wide class of technologies and sectors. In other words, can we a do a better job of “formalizing the informal,” without falling right back into the trap of trying to impose rules in a rigid, top-down, one-size-fits-all fashion?

Conclusion

Those are just a few of the hard questions we will need to consider going forward. For now, however, I think it is safe to conclude that we will no longer see much “law” being made for emerging technologies, at least not in the traditional sense of the term. Thanks to the new technological realities I have described here—and the relentless reality of the “pacing problem” more generally—I believe we are witnessing a wide-ranging and quite profound transformation in how technology is governed in our modern world. And I believe this movement away from traditional “hard law” and toward “soft law” governance mechanisms is likely to accelerate due to the increasing prevalence of innovation arbitrage, technological civil disobedience, and spontaneous private deregulation.

The ramifications of this transformation will be studied by philosophers, legal theorists, and political scientists for many decades to come. But we are still in the early years of this momentous transformation in technological governance and we will continue to struggle to figure out how to make it all work, as messy as it all may be.

[ Note: This essay is condensed from a manuscript I have been working on about The Rise of Technological Civil Disobedience. I’m not sure I will ever get around to finishing it, however, so I thought I would at least post this piece for now. In a subsequent essay, which is also part of that draft manuscript, I hope to discuss how this process might play out for technologies that are “born free” versus those that are “born in captivity.” That is, how likely is it that the trends I discuss here will take hold for technologies that have no pre-existing laws or agencies, while other technologies that are born into a regulatory environment are potentially doomed to be pigeonholed into those old regulatory regimes? What are the chances that the latter technologies can escape captivity and gain the freedom the other technologies already enjoy? How might technology-enabled “spontaneous private deregulation” be accelerated for those sectors? Is that always desirable? Again, I will leave these questions for another day. Scholars and students who are interested in these topics can feel free to contact me if they are interested in discussing them as well as potential paper ideas. Regardless of how you feel about these trends, these issues are ripe for intellectual exploration.]

[1]     Benjamin Edelman and Damien Geradin, “Spontaneous Deregulation,” Harvard Business Review, April 2016, https://hbr.org/2016/04/spontaneous-deregulation.

[2]     Megan Geuss, “After mothballing Comma One, George Hotz releases free autonomous car software,” Ars Technica, November 30, 2016, http://arstechnica.com/cars/2016/11/after-mothballing-comma-one-george-hotz-releases-free-autonomous-car-software.

[3]     See: “NHTSA Scared This Self-Driving Entrepreneur Off the Road,” Bloomberg Technology, October 28, 2016, https://www.bloomberg.com/news/articles/2016-10-28/nhtsa-scared-this-self-driving-entrepreneur-off-the-road; Sean O’Kane, “George Hotz cancels his self-driving car project after NHTSA expresses concern,” The Verge, October 28, 2016, http://www.theverge.com/2016/10/28/13453344/comma-ai-self-driving-car-comma-one-kit-canceled; Brad Templeton, “Comma.ai cancels comma-one add-on box after threats from NHTSA,” Robohub, October 31, 2016, http://robohub.org/comma-ai-cancels-comma-one-add-on-box-after-threats-from-nhtsa.

[4]     Mark Harris, “How Otto Defied Nevada and Scored a $680 Million Payout from Uber,” Backchannel, November 28, 2016,  https://backchannel.com/how-otto-defied-nevada-and-scored-a-680-million-payout-from-uber-496aa07f5ba2#.9rmtb29bl

[5]     Larry E. Hall, “Otto Self-Driving Truck Tests in Ohio; Violated Nevada Regulations,” Hybrid Cars, November 29, 2016, http://www.hybridcars.com/otto-self-driving-truck-tests-in-ohio-violated-nevada-regulations.

[6]     Kara Driscoll, “Ohio to create ‘smart’ road for driverless trucks,” Dayton Daily News, November 30, 2016, http://www.daytondailynews.com/business/ohio-create-smart-road-for-driverless-trucks/25qC7uYjz9rE96q6YFVUUK.

[7]     Brad Templeton, “Comma.ai cancels comma-one add-on box after threats from NHTSA,” Robohub, October 31, 2016, http://robohub.org/comma-ai-cancels-comma-one-add-on-box-after-threats-from-nhtsa/

[8]     Adam Thierer and Caleb Watney, “Comment on the Federal Automated Vehicles Policy,” November 22, 2016, https://www.researchgate.net/publication/311065194_Comment_on_the_Federal_Automated_Vehicles_Policy.

[9]     National Highway Traffic Safety Administration (NHTSA), Federal Automated Vehicles Policy, September 2016.

[10]   Adrienne LaFrance, “Self-Driving Cars Could Save 300,000 Lives per Decade in America,” Atlantic, September 29, 2015

[11]   Adam Thierer and Caleb Watney, “Comment on the Federal Automated Vehicles Policy,” November 22, 2016, https://www.researchgate.net/publication/311065194_Comment_on_the_Federal_Automated_Vehicles_Policy.

[12]   Templeton.

[13]   Sean O’Kane and Lauren Goode, “George Hotz is giving away the code behind his self-driving car project,” The Verge, November 30, 2016, http://www.theverge.com/2016/11/30/13779336/comma-ai-autopilot-canceled-autonomous-car-software-free.

[14]   NHTSA, Federal Automated Vehicles Policy, 76.

[15]   Adam Thierer, Jerry Brito, and Eli Dourado, “Technology Policy: A Look Ahead,” Technology Liberation Front, May 12, 2014, http://techliberation.com/2014/05/12/technology-policy-a-look-ahead.

[16]   Marc Andreessen, “Why Software Is Eating the World,” Wall Street Journal, August 20, 2011, http://www.wsj.com/articles/SB10001424053111903480904576512250915629460.

[17]   Wendell Wallach, A Dangerous Master: How to Keep Technology from Slipping beyond Our Control (New York: Basic Books, 2015), 60.

[18]   Larry Downes, The Laws of Disruption: Harnessing the New Forces That Govern Life and Business in the Digital Age 2 (2009).

[19]   Id.

[20]   Thierer, Permissionless Innovation, at 1.

[21]   Gary E. Marchant and Wendell Wallach, “Governing the Governance of Emerging Technologies,” in Gary E. Marchant, Kenneth W. Abbott & Braden Allenby (eds.), Innovative Governance Models for Emerging Technologies (Cheltenham, UK: Edward Elgar, 2013), 136.

[22]   Marc A. Saner,  “The Role of Adaptation in the Governance of Emerging Technologies,” in Gary E. Marchant, Kenneth W. Abbott & Braden Allenby (eds.), Innovative Governance Models for Emerging Technologies (Cheltenham, UK: Edward Elgar, 2013), 106.

[23]   Ibid., at 94.

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