learning – 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 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 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 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.

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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 VIDEO: My London Talk about the Future of AI Governance https://techliberation.com/2022/06/13/video-my-london-talk-about-the-future-of-ai-governance/ https://techliberation.com/2022/06/13/video-my-london-talk-about-the-future-of-ai-governance/#comments Mon, 13 Jun 2022 09:29:50 +0000 https://techliberation.com/?p=76999

On Thursday, June 9, it was my great pleasure to return to my first work office at the Adam Smith Institute in London and give a talk on the future of innovation policy and the governance of artificial intelligence. James Lawson, who is affiliated with the ASI and wrote a wonderful 2020 study on AI policy, introduced me and also offered some remarks. Among the issues discussed:

  • What sort of governance vision should govern the future of innovation generally and AI in particular: the “precautionary principle” or “permissionless innovation”?
  • Which AI sectors are witnessing the most exciting forms of innovation currently?
  • What are the fundamental policy fault lines in the AI policy debates today?
  • Will fears about disruption and automation lead to a new Luddite movement?
  • How can “soft law” and decentralized governance mechanism help us solve pressing policy concerns surrounding AI?
  • How did automation affect traditional jobs and sectors?
  • Will the European Union’s AI Act become a global model for regulation and will it have a “Brussels Effect” in terms of forcing innovators across the world to come into compliance with EU regulatory mandates?
  • How will global innovation arbitrage affect the efforts by governments in Europe and elsewhere to regulate AI innovation?
  • Can the common law help address AI risk? How is the UK common law system superior to the US legal system?
  • What do we mean by “existential risk” as it pertains to artificial intelligence?

I have a massive study in the works addressing all these issues. In the meantime, you can watch the video of my London talk here. And thanks again to my friends at the Adam Smith Institute for hosting!

Additional Reading:

 

 

]]> https://techliberation.com/2022/06/13/video-my-london-talk-about-the-future-of-ai-governance/feed/ 5 76999 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 “Learning by Doing,” the Process of Innovation & the Future of Employment https://techliberation.com/2015/09/25/learning-by-doing-the-process-of-innovation-the-future-of-employment/ https://techliberation.com/2015/09/25/learning-by-doing-the-process-of-innovation-the-future-of-employment/#comments Fri, 25 Sep 2015 19:08:37 +0000 http://techliberation.com/?p=75807

I recently finished  Learning by Doing: The Real Connection between Innovation, Wages, and Wealth , by James Bessen of the Boston University Law School. It’s a good book to check out if you are worried about whether workers will be able to weather this latest wave of technological innovation.  One of the key insights of Bessen’s book is that, as with previous periods of turbulent technological change, today’s workers and businesses will obviously need find ways to adapt to rapidly-changing marketplace realities brought on by the Information Revolution, robotics, and automated systems.

That sort of adaptation takes time, but for technological revolutions to take hold and have meaningful impact on economic growth and worker conditions, it requires that large numbers of ordinary workers acquire new knowledge and skills, Bessen notes. But, “that is a slow and difficult process, and history suggests that it often requires social changes supported by accommodating institutions and culture.” (p 223) That is not a reason to resist disruptive forms of technological change, however. To the contrary, Bessen says, it is crucial to allow ongoing trial-and-error experimentation and innovation to continue precisely because it represents a learning process which helps people (and workers in particular) adapt to changing circumstances and acquire new skills to deal with them. That, in a nutshell, is “learning by doing.” As he elaborates elsewhere in the book:

Major new technologies become ‘revolutionary’ only after a long process of learning by doing and incremental improvement. Having the breakthrough idea is not enough. But learning through experience and experimentation is expensive and slow. Experimentation involves a search for productive techniques: testing and eliminating bad techniques in order to find good ones. This means that workers and equipment typically operate for extended periods at low levels of productivity using poor techniques and are able to eliminate those poor practices only when they find something better. (p. 50)

Luckily, however, history also suggests that, time and time again, that process has happened and the standard of living for workers and average citizens alike improved at the same time.

Of course, that won’t stop some from proclaiming that,  This time it’s different! Indeed, we’re hearing increasing concerns today about the “rise of the robots,” and the general negative impact of automation on the workforce.

But these concerns are really nothing new. “There have been periodic warnings in the last two centuries that automation and new technology were going to wipe out large numbers of middle class jobs,” notes MIT economist David H. Autor. Luckily, those dire predictions have not come to pass. The reason was because short-sighted skeptics failed to appreciate how as new technologies obliterated old businesses and jobs, it simultaneously opened up many more opportunities that were impossible to predict in advance. For every factory worker that lost a job due to technological innovation, new jobs opened up in entirely new sectors that usually offered workers better wages, a safer work environment, and more leisure time. And society clearly benefited in many other ways.

In a new essay for  The Journal of Economic Perspectives on “The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?” Joel Mokyr, Chris Vickers, and Nicolas L. Ziebarth, note that “Discussions of how technology may affect labor demand are often focused on existing jobs, which can offer insights about which occupations may suffer the greatest dislocation, but offer much less insight about the emergence of as-yet-nonexistent occupations of the future.” They continue on to note that:

In the end, the fears of the Luddites that machinery would impoverish workers were not realized, and the main reason is well understood. The mechanization of the early 19th century could only replace a limited number of human activities. At the same time, technological change increased the demand for other types of labor that were complementary to the capital goods embodied in the new technologies. This increased demand for labor included such obvious jobs as mechanics to fix the new machines, but it extended to jobs for supervisors to oversee the new factory system and accountants to manage enterprises operating on an unprecedented scale. More importantly, technological progress also took the form of product innovation, and thus created entirely new sectors for the economy, a development that was essentially missed in the discussions of economists of this time.

And despite a resurgence of automation anxiety in recent years, that historic trend still generally holds true. In late 2014, economists at Deloitte LLP published a sweeping survey of the impact of technology and jobs over the past 200 years and found that “Technology has transformed productivity and living standards, and, in the process, created new employment in new sectors.” This is because human needs and wants constantly change and, therefore, “The stock of work in the economy is not fixed; the last 200 years demonstrates that when a machine replaces a human, the result, paradoxically, is faster growth and, in time, rising employment.” And they conclude that: “Machines will take on more repetitive and laborious tasks, but seem no closer to eliminating the need for human labour than at any time in the last 150 years. It is not hard to think of pressing, unmet needs even in the rich world: the care of the elderly and the frail, lifetime education and retraining, health care, physical and mental well-being.”

While it is easy for critics to highlight disruptions in some notable sectors where machines replaced human labor, fewer news reports or panicky books discuss the many new sectors where people have found new opportunities. Again, the historical evidence suggests that there are good reasons to have faith that humans will once again muddle through and prevail in the face of turbulent, disruptive change. As venture capitalist Marc Andreessen has noted when addressing the fear that automation is running amuck and that robots will eat all our jobs,

We have no idea what the fields, industries, businesses, and jobs of the future will be. We just know we will create an enormous number of them. Because if robots and AI replace people for many of the things we do today, the new fields we create will be built on the huge number of people those robots and AI systems made available. To argue that huge numbers of people will be available but we will find nothing for them (us) to do is to dramatically short human creativity. And I am way long human creativity.

Some tech critics may reject Andreessen’s bullish optimism about human resiliency, but real-world evidence already supports that his conclusion that we’ll learn to adapt to a world full of robots and robotic systems. A 2015 economic analysis from Colin Lewis, a behavioral economist who runs Robotenomics, showed that “despite the headlines, companies that have installed industrial robots are actually increasingly employing more people whilst at the same time adding more robots.” His research revealed that 1.25 million new jobs had been added by companies that make extensive use of industrial robots over the previous 6 years. He also found that this trend held among more recent disruptive firms like Amazon and Tesla Motors, but also older and more established companies like Chrysler, Daimler, Philips Electronics and others.

So, it’s worth keeping these facts in mind next time you read an article or book that declares that the sky is falling and that technological innovation is going to destroy labor markets and living standards. The entirety of human history points in the opposite direction. We should be bullish about our ability to muddle through tough times of technological change and flourish in the long run.

]]> https://techliberation.com/2015/09/25/learning-by-doing-the-process-of-innovation-the-future-of-employment/feed/ 2 75807 What Does It Mean to “Have a Conversation” about a New Technology? https://techliberation.com/2013/05/23/what-does-it-mean-to-have-a-conversation-about-a-new-technology/ https://techliberation.com/2013/05/23/what-does-it-mean-to-have-a-conversation-about-a-new-technology/#comments Thu, 23 May 2013 20:35:31 +0000 http://techliberation.com/?p=44789

My colleague Eli Dourado brought to my attention this XKCD comic and when tweeting it out yesterday he made the comment that “Half of tech policy is dealing with these people”:

The comic and Eli’s comment may be a bit snarky, but something about it rang true to me because while conducting research on the impact of new information technologies on society I often come across books, columns, blog posts, editorials, and tweets that can basically be summed up with the line from that comic: “we should stop to consider the consequences of [this new technology] before we …”  Or, equally common is the line: “we need to have a conversation about [this new technology] before we…”

But what does that really mean? Certainly “having a conversation” about the impact of a new technology on society is important. But what is the nature of that “conversation”? How is it conducted? How do we know when it is going on or when it is over?

Generally speaking, it is best to avoid guessing as to motive when addressing public policy arguments. It is better to just address the assertions or proposals set forth in someone’s work and not try to determine what motivates it or what other ulterior motives may be driving their reasoning.

Nonetheless, I can’t help but think that sometimes what the “we-need-to-have-a-conversation” crowd is really suggesting is that we need to have a conversation about how to slow or stop the technology in question, not merely talk about its ramifications.

I see this at work all the time in the field of privacy policy. Many policy wonks craft gloom-and-doom scenarios that suggest our privacy is all but dead. I’ve notice a lot more of this lately in essays about the “Internet of Things” and Google Glass in particular. (See these recent essays by Paul Bernal and Bruce Schneier for good examples). Dystopian dread drips from almost every line of these essays.

But, after conjuring up a long parade of horribles and suggesting “we need to have a conversation” about new technologies, authors of such essays almost never finish their thought. There’s no conclusion or clear alternative offered. I suppose that in some cases it is because there aren’t any easy answers. Other times, however, I get the feeling that they have an answer in mind — comprehensive regulation of new technologies in question — but that they don’t want to come out and say it because they think they’ll sound like Luddites. Hell, I don’t know and, again, I don’t want to guess as to motive. I just find it interesting that so much of the writing being done in this arena these days follows that exact model.

But here’s the other point I want to make: I don’t think we’ll ever be able to “have a conversation” about a new technology that yields satisfactory answers because real wisdom is born of experience. This is one of the many important lessons I learned from my intellectual hero Aaron Wildavsky and his pioneering work on risk and safety. In his seminal 1988 book Searching for Safety, Wildavsky warned of the dangers of the “trial without error” mentality — otherwise known as the precautionary principle approach — and he contrasted it with the trial-and-error method of evaluating risk and seeking wise solutions to it. Wildavsky argued that:

The direct implication of trial without error is obvious: If you can do nothing without knowing first how it will turn out, you cannot do anything at all. An indirect implication of trial without error is that if trying new things is made more costly, there will be fewer departures from past practice; this very lack of change may itself be dangerous in forgoing chances to reduce existing hazards … Existing hazards will continue to cause harm if we fail to reduce them by taking advantage of the opportunity to benefit from repeated trials.

This is a lesson too often overlooked not just in the field of health and safety regulation, but also in the world of information policy and this insight is the foundation of a filing I will be submitting to the FTC next week in its new proceeding on the “Privacy and Security Implications of the Internet of Things.” In that filing, I will note that, as was the case with many other new information and communications technologies, the initial impulse may be to curb or control the development of certain Internet of Things technologies to guard against theoretical future misuses or harms that might develop.

Again, when such fears take the form of public policy prescriptions, it is referred to as a “precautionary principle” and it generally holds that, because a given new technology could pose some theoretical danger or risk in the future, public policies should control or limit the development of such innovations until their creators can prove that they won’t cause any harms.

The problem with letting such precautionary thinking guide policy is that it poses a serious threat to technological progress, economic entrepreneurialism, and human prosperity. Under an information policy regime guided at every turn by a precautionary principle, technological innovation would be impossible because of fear of the unknown; hypothetical worst-case scenarios would trump all other considerations. Social learning and economic opportunities become far less likely, perhaps even impossible, under such a regime. In practical terms, it means fewer services, lower quality goods, higher prices, diminished economic growth, and a decline in the overall standard of living.

For these reasons, to the maximum extent possible, the default position toward new forms of technological innovation should be innovation allowed. This policy norm is better captured in the well-known Internet ideal of “permissionless innovation,” or the general freedom to experiment and learn through trial-and-error experimentation.

Stated differently, when it comes to new information technologies such as the Internet of Things, the default policy position should be an “ anti-Precautionary Principle.” Paul Ohm, who recently joined the FTC as a Senior Policy Advisor, outlined the concept in his 2008 article, “The Myth of the Superuser: Fear, Risk, and Harm Online.” “Fear of the powerful computer user, the ‘Superuser,’ dominates debates about online conflict,” Ohm argued, but this superuser is generally “a mythical figure” concocted by those who are typically quick to set forth worst-case scenarios about the impact of digital technology on society. Fear of such superusers and the hypothetical worst-case dystopian scenarios they might bring about prompts policy action, since “Policymakers, fearful of his power, too often overreact by passing overbroad, ambiguous laws intended to ensnare the Superuser but which are instead used against inculpable, ordinary users.” “This response is unwarranted,” Ohm says “because the Superuser is often a marginal figure whose power has been greatly exaggerated.”

Ohm gets it exactly right and he could have cited Wildavsky on the matter, who noted that, “’Worst case’ assumptions can convert otherwise quite ordinary conditions… into disasters, provided only that the right juxtaposition of unlikely factors occur.” In other words, creative minds can string together some random anecdotes or stories and concoct horrific-sounding scenarios for the future that leave us searching for preemptive to solutions to problems that haven’t even developed yet.

Unfortunately, fear of “superusers” and worst-case boogeyman scenarios are already driving much of the debate over the Internet of Things. Most of the fear and loathing involves privacy-related dystopian scenarios that envision a miserable panoptic future from which there is no escape. And that’s about the time the authors suggest “we need to have a conversation” about these new technologies — by which they really mean to suggest we need to find ways to put the genie back in the bottle or smash the bottle before the genie even gets out.

But how are we to know what the future holds? And even to the extent some critics believe they possess a techno-crystal ball that can forecast the future, why is it seemingly always the case that none of those possible futures involves humans gradually adapting and assimilating these new technologies into their lives the way they have countless times before? In my FTC filing next week, I will document examples of that process of initial resistance, gradual adaptation, and then eventual assimilation of various new information technologies into society. But I have already developed a model explaining this process and offering plenty of examples in my recent law review article, “Technopanics, Threat Inflation, and the Danger of an Information Technology Precautionary Principle,” as well as in this lengthy blog post, “Who Really Believes in ‘Permissionless Innovation’?”

In sum, the most important “conversations” we have about new technologies are the ones we have every day as we interact with those new technologies and with each other. Wisdom is born of experience, including experiences involving risk and the possibility of mistakes and accidents. Patience and an openness to permissionless innovation represent the wise disposition toward new technologies not only because it provides breathing space for future entrepreneurialism, but also because it provides an opportunity to observe both the evolution of societal attitudes toward new technologies and how citizens adapt to them.

]]> https://techliberation.com/2013/05/23/what-does-it-mean-to-have-a-conversation-about-a-new-technology/feed/ 3 44789 The Case for Internet Optimism, Part 1: Saving the Net From Its Detractors https://techliberation.com/2011/01/31/the-case-for-internet-optimism-part-1-saving-the-net-from-its-detractors/ https://techliberation.com/2011/01/31/the-case-for-internet-optimism-part-1-saving-the-net-from-its-detractors/#comments Mon, 31 Jan 2011 16:43:30 +0000 http://techliberation.com/?p=34765

Here’s the first of two essays I’ve recently penned making “The Case for Internet Optimism.” This essay was included in the book, The Next Digital Decade: Essays on the Future of the Internet (2011), which was edited by Berin Szoka and Adam Marcus of TechFreedom.  In these essays, I identify two schools of Internet pessimism: (1) “Net Skeptics,” who are pessimistic about the Internet improving the lot of mankind; and (2) “Net Lovers,” who appreciate the benefits the Net brings society but who fear those benefits are disappearing, or that the Net or openness are dying.  (Regular readers of this blog will be familiar with these themes since I sketched them out in previous essays here such as, “Are You an Internet Optimist or Pessimist?” and “Two Schools of Internet Pessimism.”) The second essay is here.

This essay focuses on the first variant of Internet pessimism, which is rooted in general skepticism about the supposed benefits of cyberspace, digital technologies, and information abundance. The proponents of this pessimistic view often wax nostalgic about some supposed “good ‘ol days” when life was much better (although they can’t seem to agree when those were). At a minimum, they want us to slow down and think twice about life in the Information Age and how it’s personally affecting each of us.  Occasionally, however, this pessimism borders on neo-Ludditism, with some proponents recommending steps to curtail what they feel is the destructive impact of the Net or digital technologies on culture or the economy.  I identify the leading exponents of this view of Internet pessimism and their major works. I trace their technological pessimism back to Plato but argue that their pessimism is largely unwarranted. Humans are more resilient than pessimists care to admit and we learn how to adapt to technological change and assimilate new tools into our lives over time. Moreover, were we really better off in the scarcity era when we were collectively suffering from information poverty?  Generally speaking, despite the challenges it presents society, information abundance is a better dilemma to be facing than information poverty.  Nonetheless, I argue, we should not underestimate or belittle the disruptive impacts associated with the Information Revolution.  But we need to find ways to better cope with turbulent change in a dynamist fashion instead of attempting to roll back the clock on progress or recapture “the good ‘ol days,” which actually weren’t all that good.

Down below, I have embedded the entire chapter in a Scribd reader, but the essay can also be found on the TechFreedom website for the book as well as on SSRN.  I have also includes two updated tables that appeared in my old “optimists vs. pessimists” essay.  The first lists some of the leading Internet optimists and pessimists and their books. The second table outlines some of the major lines of disagreement between these two camps and I divided those disagreements into (1) Cultural / Social beliefs vs. (2) Economic / Business beliefs.

The Case for Internet Optimism Part 1 – Saving the Net From Its Detractors (Adam Thierer) http://d1.scribdassets.com/ScribdViewer.swf

______

Theuthian Technophiles ( “The Internet Optimists”)

Thamusian Technophobes ( “The Internet Pessimists”)

Optimists

Pessimists

Cultural / Social beliefs
Net is participatory Net is polarizing
Net facilitates personalization (welcome of “Daily Me” that digital tech allows) Net facilitates fragmentation (fear of the “Daily Me”)
“a global village” balkanization and fears of “mob rule”
heterogeneity / encourages diversity of thought and expression homogeneity / Net leads to close-mindedness
allows self-actualization diminishes personhood
Net a tool of liberation & empowerment Net a tool of frequent misuse & abuse
Net can help educate the masses dumbs down the masses
anonymous communication encourages vibrant debate + whistleblowing (a net good) anonymity debases culture & leads to lack of accountability
welcome information abundance; believe it will create new opportunities for learning concern about information overload; esp. impact on learning & reading
Economic / Business beliefs
benefits of “Free” (increasing importance of “gift economy”) costs of “Free” (“free” = threat to quality & business models)
mass collaboration is generally more important individual effort is generally more important
embrace of “amateur” creativity superiority of “professionalism”
stress importance of “open systems” of production stress importance of “proprietary” models of production
“wiki” model = wisdom of crowds; benefits of crowdsourcing “wiki” model = stupidity of crowds; collective intelligence is oxymoron; + “sharecropper” concern about exploitation of free labor

Theuthian Technophiles ( “The Internet Optimists”)

Thamusian Technophobes ( “The Internet Pessimists”)

· Nicholas Negroponte, Being Digital (1995)

· Kevin Kelly, Out of Control: The New Biology of Machines, Social Systems, and the Economic World (1995)

· Virginia Postrel, The Future and Its Enemies (1998)

· James Surowiecki, The Wisdom of Crowds (2004)

· Chris Anderson, The Long Tail: Why the Future of Business is Selling Less of More (2006)

· Steven Johnson, Everything Bad is Good For You (2006)

· Glenn Reynolds, An Army of Davids: How Markets and Technology Empower Ordinary People to Beat Big Media, Big Government, and Other Goliaths (2006)

· Yochai Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom (2006)

· Clay Shirky, Here Comes Everybody: The Power of Organizing without Organizations (2008)

· Don Tapscott & Anthony D. Williams, Wikinomics: How Mass Collaboration Changes Everything (2008)

· Jeff Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business (2008)

· Tyler Cowen, Create Your Own Economy: The Path to Prosperity in a Disordered World (2009)

· Dennis Baron, A Better Pencil: Readers, Writers, and the Digital Revolution (2009)

· Jeff Jarvis, What Would Google Do ? (2009)

· Clay Shirky, Cognitive Surplus: Creativity and Generosity in a Connected Age (2010)

· Nick Bilton, I Live in the Future & Here’s How It Works (2010)

· Kevin Kelly, What Technology Wants (2010)

· Neil Postman, Technopoly: The Surrender of Culture to Technology (1993)

· Sven Birkerts, The Gutenberg Elegies: The Fate of Reading in an Electronic Age (1994)

· Clifford Stoll, High-Tech Heretic: Reflections of a Computer Contrarian (1999)

· Cass Sunstein, Republic.com (2001)

· Todd Gitlin, Media Unlimited: How the Torment of Images and Sounds Overwhelms Our Lives (2002)

· Todd Oppenheimer, The Flickering Mind: Saving Education from the False Promise of Technology (2003)

· Andrew Keen, The Cult of the Amateur: How Today’s Internet is Killing our Culture (2007)

· Steve Talbott, Devices of the Soul: Battling for Our Selves in an Age of Machines‎ (2007)

· Nick Carr, The Big Switch: Rewiring the World, from Edison to Google (2008)

· Lee Siegel, Against the Machine: Being Human in the Age of the Electronic Mob (2008)

· Mark Bauerlein, The Dumbest Generation: How the Digital Age Stupefies Young Americans and Jeopardizes Our Future (2008)

· Mark Helprin, Digital Barbarism: A Writer’s Manifesto (2009)

· Maggie Jackson, Distracted: The Erosion of Attention and the Coming Dark Age (2009)

· John Freeman, The Tyranny of E-Mail: The Four-Thousand-Year Journey to Your Inbox (2009)

· Jaron Lanier, You Are Not a Gadget (2010)

· Nick Carr, The Shallows: What the Internet Is Doing to Our Brains (2010)

· William Powers, Hamlet’s BlackBerry: A Practical Philosophy for Building a Good Life in the Digital Age (2010)
]]> https://techliberation.com/2011/01/31/the-case-for-internet-optimism-part-1-saving-the-net-from-its-detractors/feed/ 10 34765 Are You An Internet Optimist or Pessimist? The Great Debate over Technology’s Impact on Society https://techliberation.com/2010/01/31/are-you-an-internet-optimist-or-pessimist-the-great-debate-over-technology%e2%80%99s-impact-on-society/ https://techliberation.com/2010/01/31/are-you-an-internet-optimist-or-pessimist-the-great-debate-over-technology%e2%80%99s-impact-on-society/#comments Sun, 31 Jan 2010 18:47:50 +0000 http://techliberation.com/?p=25554

[I’ve been working on an outline for a book I hope to write surveying technological skepticism throughout history. I first started thinking about this topic two years when I noticed that a great number of recent books about Internet policy could generally be grouped into one of two camps: Internet optimists vs. Internet pessimists. I subsequently penned an essay on the subject that generated a fair bit of attention. So, I figured I must be on to something, and the more Net policy books I read, the more I realized that the divisions between these two camps were growing wider and increasingly heated. Thus, I thought I would share this very rough draft (much of it still in outline form) of the opening chapter of that book I want to write about this great intellectual war over the impact of technology on society. I invite reader input. Update Jan. 2011: I finally published a full-length essay on this topic. You can find it here. ]

__________

The impact of technological change on culture, learning, and morality has long been the subject of intense debate, and every technological revolution brings out a fresh crop of both pessimists and pollyannas. Indeed, a familiar cycle has repeat itself throughout history whenever new modes of production (from mechanized agriculture to assembly-line production), means of transportation (water, rail, road, or air), energy production processes (steam, electric, nuclear), medical breakthroughs (vaccination, surgery, cloning), or communications techniques (telegraph, telephone, radio, television) have appeared on the scene.

The cycle goes something like this. A new technology appears. Those who fear the sweeping changes brought about by this technology see a sky that is about to fall. These “techno-pessimists” predict the death of the old order (which, ironically, is often a previous generation’s hotly-debated technology that others wanted slowed or stopped).  Embracing this new technology, they fear, will result in the overthrow of traditions, beliefs, values, institutions, business models, and much else they hold sacred.

The pollyannas, by contrast, look out at the unfolding landscape and see mostly rainbows in the air. Theirs is a rose-colored world in which the technological revolution du jour is seen as improving the general lot of mankind and bringing about a better order.  If something has to give, then the old ways be damned! For such “techno-optimists,” progress means some norms and institutions must adapt—perhaps even disappear—for society to continue its march forward.

Our current Information Revolution is no different. It too has its share of techno-pessimists and techno-optimists. Indeed, before most of us had even heard of the Internet, people were already fighting about it—or at least debating what the rise of the Information Age meant for our culture, society, and economy.

Web 1.0 Fight: Postman vs. Negroponte

In his 1992 anti-technology screed Technopoly: The Surrender of Culture to Technology, the late social critic Neil Postman greeted the unfolding Information Age with a combination of skepticism and scorn.  Indeed, Postman’s book was a near-perfect articulation of the techo-pessimist’s creed.  “Information has become a form of garbage,” he claimed, “not only incapable of answering the most fundamental human questions but barely useful in providing coherent direction to the solution of even mundane problems.”  If left unchecked, Postman argued, America’s new technopoly—“the submission of all forms of cultural life to the sovereignty of technique and technology”—would destroy “the vital sources of our humanity” and lead to “a culture without a moral foundation” by undermining “certain mental processes and social relations that make human life worth living.”

Postman opened his polemic with the well-known allegorical tale from Plato’s Phaedrus about the dangers of the written word.  Postman reminded us how King Thamus responded to the god Theuth, who boasted of how his invention of writing would improve the wisdom and memory of the masses relative to the oral tradition of learning.  King Thamus shot back, “the discoverer of an art is not the best judge of the good or harm which will accrue to those who practice it.”  King Thamus then passed judgment himself about the impact of writing on society, saying he feared that the people “will receive a quantity of information without proper instruction, and in consequence be thought very knowledgeable when they are for the most part quite ignorant.”

And so Postman—fancying himself a bit of a modern King Thamus—cast judgment on today’s comparable technological advances and those who would glorify them:

we are currently surrounded by throngs of zealous Theuths, one-eyed prophets who see only what new technologies can do and are incapable of imagining what they will undo. We might call such people Technophiles. They gaze on technology as a lover does on his beloved, seeing it as without blemish and entertaining no apprehension for the future. They are therefore dangerous and to be approached cautiously. … If one is to err, it is better to err on the side of Thamusian skepticism.

Nicholas Negroponte begged to differ. An unapologetic Theuthian technophile, the former director of the MIT Media Lab responded on behalf of the techno-optimists in 1995 with his prescient polemic, Being Digital.  It was a paean to the Information Age, for which he served as one of the first high prophets—with Wired magazine’s back page frequently serving as his pulpit during the many years he served as a regular columnist.

Appropriately enough, the epilogue of Negroponte’s Being Digital was entitled “An Age of Optimism” and, like the rest of the book, it stood in stark contrast to Postman’s pessimistic worldview.  Although Negroponte conceded that technology indeed had a “dark side” in that it could destroy much of the old older, he believed that was inevitable, but also not cause for much concern. “Like a force of nature, the digital age cannot be denied or stopped,” he insisted, and we must learn to appreciate the ways “digital technology can be a natural force drawing people into greater world harmony.” (This sort of techno-determism is a theme we would see on display in many of the works by other Internet optimists that followed in Negroponte’s footsteps.)

To Postman’s persistent claim that America’s technopoly lacked a moral compass, Negroponte again conceded the point but took the glass-is-half-full view: “Computers are not moral; they cannot resolve complex issues like the rights to life and to death. But being digital, nevertheless, does give much cause for optimism.”  His defense of the digital age rested on the “four very powerful qualities that will result in its ultimate triumph: decentralizing, globalizing, harmonizing, and empowering.” Gazing into his techno-crystal ball in 1995, Negroponte forecast the ways in which those qualities would revolutionize society:

The access, the mobility, and the ability to effect change are what will make the future so different from the present. The information superhighway may be mostly hype today, but it is an understatement about tomorrow. It will exist beyond people’s wildest predictions. As children appropriate a global information resource, and as they discover that only adults need learner’s permits, we are bound to find new hope and dignity in places where very little existed before.

In many ways, that’s the world we occupy today; a world of unprecedented media abundance and unlimited communications and connectivity opportunities.

But the great debate about the impact of digitization and information abundance would not end with Postman and Negroponte. Theirs would only be Act I in a drama that continues to unfold, and it is growing more heated and complex with each new character that comes on the stage.

Web War II

 

The disciples of Postman and Negroponte are a colorful, diverse lot. The players in Act II of this drama occupy many diverse professions—journalists, technologists, business consultants, sociologists, economists, lawyers, etc.—and they are disagreeing even more vehemently and vociferously about the impact of the Internet and digital technologies than Postman and Negroponte did.

In Exhibit 1, I have listed the Internet optimists and pessimists and list their key works.

Theuthian Technophiles (aka “The Internet Optimists”) Thamusian Technophobes (aka “The Internet Pessimists”)
Nicholas Negroponte, Being Digital Neil Postman, Technopoly: The Surrender of Culture to Technology

Virginia Postrel, The Future and Its Enemies

 Andrew Keen, The Cult of the Amateur: How Today’s Internet is Killing our Culture
James Surowiecki, The Wisdom of Crowds Lee Siegel, Against the Machine: Being Human in the Age of the Electronic Mob
Clay Shirky, Here Comes Everybody: The Power of Organizing without Organizations and Cognitive Surplus: Creativity and Generosity in a Connected Age Nick Carr, The Big Switch: Rewiring the World, from Edison to Google and The Shallows: What the Internet Is Doing to Our Brains
Yochai Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom Mark Helprin, Digital Barbarism: A Writer’s Manifesto
Chris Anderson, The Long Tail: Why the Future of Business is Selling Less of More Cass Sunstein, Republic.com
Kevin Kelly,Out of Control: The New Biology of Machines, Social Systems, and the Economic World Todd Gitlin, Media Unlimited: How the Torment of Images and Sounds Overwhelms Our Lives
Jeff Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business Mark Bauerlein, The Dumbest Generation: How the Digital Age Stupefies Young Americans and Jeopardizes Our Future (Or, Don’t Trust Anyone Under 30)
Don Tapscott & Anthony D. Williams, Wikinomics: How Mass Collaboration Changes Everything Steve Talbott, Devices of the Soul: Battling for Our Selves in an Age of Machines‎
Jeff Jarvis, What Would Google Do John Freeman, The Tyranny of E-Mail: The Four-Thousand-Year Journey to Your Inbox
Tyler Cowen, Create Your Own Economy: The Path to Prosperity in a Disordered World Jaron Lanier, You Are Not a Gadget
Dennis Baron, A Better Pencil: Readers, Writers, and the Digital Revolution David Trend, The End of Reading: From Gutenberg to Grand Theft Auto

In Exhibit 2, I have sketched out the major lines of disagreement between these two camps and divided those disagreements into (1) Cultural / Social beliefs vs. (2) Economic / Business beliefs.

Optimists Pessimists

Cultural / Social beliefs
Net is participatory Net is polarizing
Net facilitates personalization (welcome of “Daily Me” that digital tech allows) Net facilitates fragmentation (fear of the “Daily Me”)
“a global village” balkanization and fears of “mob rule”
heterogeneity / encourages diversity of thought and expression homogeneity / Net leads to close-mindedness
allows self-actualization diminishes personhood
Net a tool of liberation & empowerment Net a tool of frequent misuse & abuse
believe Net can help educate fear dumbing-down of masses
anonymous communication is a net good; encourages vibrant debate + whistleblowing fear of anonymity; say it debases culture & leads to lack of accountability
welcome information abundance; believe it will create new opportunities for learning concern about information overload; esp. impact on learning & reading
Economic / Business beliefs
benefits of “Free” (increasing importance of “gift economy”) costs of “Free” (“free” = threat to quality & business models)
mass collaboration is generally more important individual effort is generally more important
embrace of “amateur” creativity superiority of “professionalism”
superiority of “open systems” of production superiority of “proprietary” models of production
“wiki” model = wisdom of crowds; benefits of crowdsourcing “wiki” model = stupidity of crowds; collective intelligence is oxymoron; + “Sharecropper” concern @ exploiting free labor

When you boil it all down, there are two major points of contention between the optimists and pessimists:

  1. The impact of technology on learning & culture & the role of experts vs. amateurs in that process.
  2. The promise—or perils—of personalization.

The Debate over Learning & Culture

  • Internet optimists and pessimists have engaged in heated debates over role of amateur production and benefits of abundant media
  • pessimists fear impact of Net and “cult of amateur” on “professional” media
  • without “enforceable scarcity” and protection for the “enlightened class,” the pessimists wonder how “high quality” news or “high art” will get funded and disseminated; and they worry about the decline of authority & truth
  • optimists argue that new modes of production (namely peer-production) will be an adequate (if not superior) alternative
    • or they believe new business models will evolve to support professional media
  • but pessimists argue that all the new choices are largely false choices
    • participatory democracy all bunk (“mob rule” and rumor mill mongering)
    • just more force-fed commercial propaganda; concerns about advertising
    • also worry about “digital sharecropping” where small group of elites make money off backs of free labor
  • optimists counter that Web 2.0 offers real choices and voices
    • optimists argue that many (perhaps most) aren’t in it for the money
    • they do it for love of knowledge & “free culture”
  • pessimists argue that “free” culture isn’t free at all; often just parasitic copying / piracy
    • could have profound ramifications for future of news, journalism, “high culture”
    • fear loss of trusted intermediaries & authorities
    • could “dumb down” the masses
  • the centrality of Wikipedia to the discussion serves as a microcosm of the entire debate
    • does Wikipedia mark the decline of authority?
    • what is “truth,” the pessimists ask? [“truthiness” fear, a la S. Colbert & Manjoo]
    • who and what can be trusted if everyone is considered an authority?
    • on the other hand, what if it works (at least reasonably well)?
    • what does that tell us about peer production / crowdsourcing?

The Debate over the Promise or Perils of Personalization

  • both optimists and pessimists agree that Net & Web 2.0 is leading to more “personalized” media experience
    • but they vehemently disagree on whether that is good or bad
    • what will it mean for participatory democracy?
  • pessimists fear Negroponte’s “Daily Me” (i.e., hyper-personalization) leads to:
    • homogenization
    • close-mindedness
    • an online echo-chamber
    • overload of choices + just more corporate brainwashing
  • optimists counter that personalization leads to:
    • heterogeneity / chance for everyone to be heard
    • openness
    • exposure to new thinking and opinions
    • abundance of choices = diversity of thought / participation
  • in the extreme, some pessimists fear the “mechanization of the soul” and the “surrender to the machine”
  • while that may sound a bit over the top, it doesn’t help that some optimists speak of the noosphere & “global consciousness” and seem to long for the eventual singularity

Who’s Got It Right?

  • On balance, I believe the optimists generally have the better of the argument today
  • But pessimists make many fair points that deserve to be taken seriously; they just need a more reasonable articulation of (some of) those concerns
  • The better approach is what I call “pragmatic optimism,” which attempts to rid the optimist paradigm of its kookier, pollyannish thinking while also taking into account some of the very legitimate concerns raised by the pessimists, but rejecting its Luddite fringe in the process.

Thoughts on the Pessimists…

  • First and foremost, the pessimists need better spokespersons! Or, they at least need a more moderated, less hysterical tone when addressing concerns raised by technological progress (many of which are quite legitimate).
  • It’s often difficult to take the pessimists seriously when they persist with their seeming outright hostility to most forms of technological progress / change. Every one of them claim they are not a Luddite, and often I believe them. But the tone of some of their writing, and the thrust of some of their recommendations, have clear Luddite tendencies.
  • Moreover, their endless name-calling and derision for the digital generation is, at times, just as insulting and immature as they “mob” they repeatedly castigate in their works. Too often, their criticism devolves into philosophical snobbery and blatant elitism. Constantly looking down their noses at digital natives and all “amateur” production doesn’t help them win any converts.
  • It’s quite shocking how the pessimists have almost nothing good to say about Wikipedia and demonize it endlessly. Much the same goes for open source and other collaborative efforts. They don’t appear willing to accept the possibility of any benefits coming from collective efforts. And they wrongly treat the rise of collective / collaborative efforts as a zero-sum game; they seem to imagine it represents a net loss of individual effort & “personhood.” That simply doesn’t follow.
  • Most importantly, the pessimists need to come to grips with the Information Revolution and offer more constructive and practical solutions to legitimately difficult transitional problems created by disintermediating influences of the digital technologies and Net.
  • The nostalgia the pessimists typically espouse for the past is a common refrain of cultural and technological critics who fear that the “good ‘ol days” are behind us and the current good-for-nothing generation and their new-fangled gadgets are steering us straight into a moral abyss.  The truth typically proves less cataclysmic, of course.  The great thing about humans is that we adapt better than other creatures. When it comes to technological change, resiliency is hard-wired into our genes.  We learn how to use the new tools that are given to us and gradually assimilate them into our lives and culture.  Indeed, we have lived through more radical revolutions than the Information Revolution. We can adapt and learn to live with some of the legitimate difficulties & downsides of the Information Age.
  • The pessimists are at their best when highlighting the very legitimate concerns about the challenges that accompany technological change, including the impact of the digital revolution on “professional” media and the decline of authority among trusted experts and intermediaries.
    • we absolutely don’t want to lose all that
    • there are real benefits associated with it
    • and we need to find a way to fund “professional” media / art going forward
  • But, practically speaking, what would the pessimists have us do if we can’t mitigate these problems? Would they roll back the clock with burdensome restrictions? As Ben Casnocha noted recently: “the wind at the backs of all techno-optimists … [is] the forward momentum of technological development. You cannot turn back the clock. It is impossible to envision a future where there is less information and fewer people on social networks. It is very possible to envision increasing abundance along with better filters to manage it. The most constructive contributions to the debate, then, heed Moore’s Law in the broadest sense and offer specific suggestions for how to harness the change for the better.”  That’s what many pessimists have failed to do in their works.

Thoughts on the Optimists…

  • The optimists currently have the better of the debate as the abundance of Web 2.0 riches is generally benefiting culture / society.
  • Relative to the past it is almost impossible to see how one could argue society has not benefited from the Internet and new digital technologies. The Digital Revolution has greatly empowered masses and offered them more informational inputs.
  • An age of abundance is certainly preferable to an age of information scarcity!
  • But optimists need to be less Pollyanna-ish and avoid becoming the “technopolists”  (or digital utopians) that Postman feared were taking over our society
    • Way too much Rousseauian romanticism at work in some optimist writings. All this talk of the Net “remaking man” or human nature is pure rubbish.
    • Not all change is good change; the optimists need to be mature enough to understand and address the occasional downsides of digital life without dismissing the critics.
    • And they need to acknowledge that sometimes the wisdom of crowds really can = the stupidity of crowds (when does collective intelligence devolve into herd mentality?) And all this crazy talk of “the hive mind” and the “noosphere” must end.  Some of optimists sound like they long for life in The Matrix; bring on the Singularity!  That’s when you know an optimists has crossed over into the realm of quixotic techno-utopianism.
  • Optimists often overplay the benefits of collective intelligence, collaboration, and the role of amateur production.  They need to frame Wiki / peer-production models as a complement to professional media, not a replacement for it.
    • Could The New York Times really be cobbled together by amateurs each day?
    • Why aren’t there any really compelling open source video games?
    • There is a big difference between “remix culture” and “rip-off culture”
    • “The Long Tail” is not “the future of all business”; but it is an increasingly important part of it, and it is wonderful that it is so much more accessible than it was in the past.
    • Will we really be better off if all professionals & intermediaries disappear? Optimists play the “old media just don’t get it” card too often and snobbishly dismiss all their concerns and efforts to reinvent themselves
  • Optimists need to place technological progress in context and appreciate that, as Postman argued, there are some moral dimensions to technological progress that deserve attention.
  • Of course, on the other hand, some of those moral consequences are profoundly positive, which the pessimists usually fail to appreciate or even acknowledge.

Conclusion: Toward “Pragmatic Optimism”

 

  • Generally speaking, I believe the optimists currently have the better of the debate. It is impossible for me to believe that we were better off in an era of information poverty & un-empowered masses.
  • But there’s a kernel of truth to what the pessimists predict about how the passing of the old order leaving society without some things that might be worth preserving.  And they are certainly correct that each of us should think about how to better balance new technologies and assimilate them into our lives.
  • The sensible middle ground position is “pragmatic optimism”: We should embrace the amazing technological changes at work in today’s Information Age but do so with a healthy dose of humility and appreciation for the disruptive impact and pace of that change. [See my “Pragmatic (Internet) Optimist’s Creed” below]
  • We need to think about how to mitigate the negative impacts associated with technological change without adopting the paranoid tone or Luddite-ish recommendations of the pessimists.
  • And it is important for us to personally exercise some personal restraint in terms of the role technology plays in our life. While pessimists from Plato and Postman certainly went too far, there is a kernel of truth to their claim that, taken to an extreme, technology can have a negative impact on life and learning.  We need to focus on the Aristotelian mean. We must avoid neo-Luddite calls for a return to “the good ‘ol days” on the one hand, while also rejecting techno-utiopian Pollyanna-ism on the other
  • Regardless, the old Theuth-Thamus debate about the relationship between technological change and its impact on culture and society will continue to rage. There is no chance this debate will die down anytime soon. And just wait till virtual reality goes mainstream!  Oh brother, now that is going to be a lively debate. I might turn into a Thamusian once I find my son playing a virtual gangster or pimp in “Grand Theft Auto 12: The Immersive Experience.”
  • Nonetheless, generally speaking, I remain quite bullish about the prospects for technology to generally improve the human condition.

The Pragmatic (Internet) Optimist’s Creed

by Adam Thierer

I believe that the Internet and digital technologies are reshaping our culture, economy, and society in most ways for the better, but not without some serious heartburn along the way.

I believe that the world of information abundance that has dawned is vastly superior to the world of information poverty that we just left. But I also understand that not all information is equal and that that the rise of abundance raises concerns about information overload, objectionable content, and the role of “authority” and “truth.”

I believe the era of traditional Mass Media is coming to an end, but “professional” media institutions and creators continue to play a vital role in the creation, aggregation, and dissemination of news, information, culture, and entertainment. The Internet, however, will force gut-wrenching changes on traditional media institutions and some of the more traditionally vital ones (ex: daily local newspapers) will struggle to re-invent themselves, or may wither away entirely. And while I believe that “professional” journalism faces very serious challenges from the rise of the Internet and user-generated content, but I also believe that hybrid forms of news-gathering and reporting are offering society exciting new ways to learn about the world around them.

I believe Wikipedia is an amazing example of collection action / intelligence at work, but I also understand it is not without flaws and limitations. I believe Wikipedia is a wonderful complement, but not a complete substitute, for other media and information sources and inputs.

I believe that free and open source software (FOSS) has produced enormous social / economic benefits, but I do not believe that FOSS (or “wiki” models) will replace all proprietary business models or methods.  Each model or mode of production has its place and purpose and they will continue to co-exist going forward, albeit in serious tension at times.

I believe the Long Tail is a powerful phenomenon, but not “the future of all business.” It is now a more important part of the future of business, but not the entirety of it. But it is wonderful that it is more accessible than ever and that we have found ways to monetize it to benefit less well know creators and innovators.

I believe there is a difference between “remix culture” and “ripoff culture.”  Remix culture generally enhances and extends culture and creativity. Blatant content piracy, on the other hand, can discourage the creative efforts of the citizenry and deprive some of society’s most gifted creators of the incentive to produce culturally beneficial works. Likewise, hacking, circumvention, and reverse-engineering all play an important and legitimate role in our new digital economy, but one need not accept the legitimacy of those activities when conducted for nefarious purposes (think identity theft or chip-modding to facilitate video game piracy.)

I believe that the Internet has empowered the masses and created a world of “pro-sumers” that gives every man, woman, and child a soapbox on which to speak to the world. But that does not mean that all of them will have something interesting to say, and I won’t praise user-generated content as a good in and of itself. It’s quality, not volume, that counts.

I believe that the Internet’s empowering nature has changed much about society and culture, but I do not believe in the romanticism some espouse about how the Net “remaking man” or changing human nature in any fundamental way. The Internet does not liberate us from all earthly constraints and it cannot magically solve all of civilization’s problems.

I believe that the Internet is reinvigorating deliberative democracy and giving us increased exposure to a breathtaking diversity of views previously inaccessible. On the other hand, I understand that some will often seek out only those views that reinforce their pre-existing biases.

I believe in the liberating power of freedom of speech and expression, and appreciate that the Internet and the rise of user-generated content has given us a world of unprecedented information and cultural riches. I also understand, however, that unrestricted freedom of speech and expression permits an increase in the prevalence of objectionable, even loathsome, speech and content. On net, however, (excuse the pun) the Internet is the most important medium of human communication and expression yet.

In sum, there are more reasons to be optimistic than pessimistic about the Internet and its role in shaping our lives, culture, economy, and society. But that doesn’t mean it will be all roses going forward.

­­­­___

Additional Reading (from me):

Additional Reading (from others):

  • and here’s a great video from 1995 featuring the late Neil Postman with his pessimistic take on cyberspace..

Also, courtesy of the Brain Pickings blog, check out this amazing 1972 documentary based on Alvin Toffler’s famous 1970 book, Future Shock. It perfectly foreshadowed so many of today’s technology policy debates.

]]> https://techliberation.com/2010/01/31/are-you-an-internet-optimist-or-pessimist-the-great-debate-over-technology%e2%80%99s-impact-on-society/feed/ 194 25554 review: A Better Pencil by Dennis Baron https://techliberation.com/2009/10/23/review-a-better-pencil-by-dennis-baron/ https://techliberation.com/2009/10/23/review-a-better-pencil-by-dennis-baron/#comments Fri, 23 Oct 2009 21:59:43 +0000 http://techliberation.com/?p=22849

A Better Pencil book coverI very much enjoyed Dennis Baron’s new book, A Better Pencil: Readers, Writers, and the Digital Revolution, and highly recommend you pick it up. Baron does a wonderful job exploring the history of techno-pessimism and the endless battles about the impact of new technologies on life and learning, something I have written about here before in my essays on “Internet optimists vs. pessimists” (See: 1, 2, 3).

I have a complete review of Baron’s A Better Pencil now up on the City Journal‘s website here.  I’ve also pasted it down below.

Plato Wrote it Down by Adam Thierer

a review of A Better Pencil: Readers, Writers, and the Digital Revolution, by Dennis Baron (Oxford University Press, 280 pp., $24.95)

In the beginning, Dennis Baron reminds us in his new book, A Better Pencil, there was the word—the spoken word, that is. Oral tradition, the passing of knowledge through stories and lectures, was the primary method of instruction and learning throughout early human civilization. But then a few innovative souls decided to start writing everything down on stones and clay. Almost as soon as they did, a great debate began on the impact of new communications technology on culture and education. And it rages on today, with a new generation of optimists and skeptics battling over the impact that computing, the Internet, and digital technologies have on our lives and on how we learn about the world.

Baron, a professor of English and linguistics at the University of Illinois, begins his splendid history of these debates with the well-known tale from Plato’s Phaedrus about the dangers of the written word. The Egyptian god Theuth boasts to King Thamus about how his invention of writing will improve the wisdom and memory of the masses. Thamus shoots back, “The discoverer of an art is not the best judge of the good or harm which will accrue to those who practice it.” Thamus then passes judgment on writing’s impact on society, saying he fears that the people “will receive a quantity of information without proper instruction, and in consequence be thought very knowledgeable when they are for the most part quite ignorant.”

Of course, as Baron points out, we remember this warning only “because Plato wrote it down.” It’s one of the recurrent ironies in the history of techno-skepticism that while “the shock of the new often brings out critics eager to warn us away,” those critics often embrace—or, at the very least, benefit from—the very tools that they want the rest of us to shun. Whether it’s Luddites On-Line winning Yahoo’s “Cool Site of the Day” award, or the Writing Instrument Manufacturers Association promoting National Handwriting Day via the Internet, or Ted Kaczynski’s Unabomber Manifesto attracting unprecedented readership thanks to its availability on the Web, those who have a “common tendency to romanticize the good old ways” of doing things often fail to appreciate how new technology can benefit society—including themselves.

Baron walks us through a litany of historical examples—the printing press, the telegraph, telephones, typewriters, pocket calculators, personal computers, word processors, webpages, blogs, social-networking sites, and more—and identifies the usual pattern: we greet each new technology with deep distrust and dire warnings, but in time we adapt to the new realities. Indeed, as a species, we have an unparalleled ability to learn new ways of doing things. We don’t always like technological change, and often we deeply resent or fear it, but in the end, we learn to live with it and eventually to embrace it.

With the rise of the Internet and digital technologies, we see this pattern unfolding once again. “According to the latest generation of critics and naysayers,” Baron notes, “today it is computers that are producing texts whose value and credibility we question; computers that are giving too many people control over the creation and publication of text; computers that are wreaking havoc with our handwriting.” Contemporary critics also fret over “information overload.”

The backlash against computers and digitization began while the Internet was still in its cradle, with the 1992 publication of Neil Postman’s anti-technology screed, Technopoly: The Surrender of Culture to Technology. Postman’s intellectual descendants include Internet critics such as Lee Siegel, Andrew Keen, and Mark Helprin, whose works drip with disdain for all things digital. They warn of a coming dystopia where truth and authority vanish, culture crumbles, and political polarization breeds closed-mindedness and even the death of deliberative democracy.

These overly pessimistic critics turn a blind eye to both the wonders of the digital age and humanity’s ability to adapt. As Baron persuasively argues, “English survives, conversation thrives online as well as off, and on balance, digital communications seems to be enhancing human interaction, not detracting from it.” In fact, we live in a world of unprecedented media abundance that previous generations would have found unimaginable. As Baron puts it: “The Internet is a true electronic frontier where everyone is on his or her own: all manuscripts are accepted for publication, they remain in virtual print forever, and no one can tell writers what to do.” Such human empowerment is worth celebrating, even if it does have the occasional downside. Abundance is better than a world of scarce choices and few voices.

Baron’s retelling of the history of techno-skepticism is edifying, but it leaves one with the nagging feeling that these debates will never cease. Each generation will witness a technological watershed that brings out a fresh crop of both pollyannas and pessimists. Like Plato, however, most of us will embrace whatever’s next and move forward.

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