Over at Discourse magazine I’ve posted my latest essay on how conservatives are increasingly flirting with the idea of greatly expanding regulatory control of private speech platforms via some sort of common carriage regulation or new Fairness Doctrine for the internet. It begins:

Conservatives have traditionally viewed the administrative state with suspicion and worried about their values and policy prescriptions getting a fair shake within regulatory bureaucracies. This makes their newfound embrace of common carriage regulation and media access theory (i.e., the notion that government should act to force access to private media platforms because they provide an essential public service) somewhat confusing. Recent opinions from Supreme Court Justice Clarence Thomas as well as various comments and proposals of Sen. Josh Hawley and former President Trump signal a remarkable openness to greater administrative control of private speech platforms. Given the takedown actions some large tech companies have employed recently against some conservative leaders and viewpoints, the frustration of many on the right is understandable. But why would conservatives think they are going to get a better shake from state-regulated monopolists than they would from today’s constellation of players or, more importantly, from a future market with other players and platforms?

I continue on to explain why conservatives should be skeptical of the administrative state being their friend when it comes to the control of free speech. I end by reminding conservatives what President Ronald Reagan said in his 1987 veto of legislation to reestablish the Fairness Doctrine: “History has shown that the dangers of an overly timid or biased press cannot be averted through bureaucratic regulation, but only through the freedom and competition that the First Amendment sought to guarantee.”

Read more at Discourse, and down below you will find several other recent essays I’ve written on the topic.

• “FCC’s O’Rielly on First Amendment & Fairness Doctrine Dangers“
• “A Good Time to Re-Read Reagan’s Fairness Doctrine Veto“
• “Sen. Hawley’s Radical, Paternalistic Plan to Remake the Internet“
• “How Conservatives Came to Favor the Fairness Doctrine & Net Neutrality“
• “Sen. Hawley’s Moral Panic Over Social Media“
• “The White House Social Media Summit and the Return of ‘Regulation by Raised Eyebrow’“
• “The Not-So-SMART Act“
• “The Surprising Ideological Origins of Trump’s Communications Collectivism“

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

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

Here is the abstract:

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

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

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

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

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

Background

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

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

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

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

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

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

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

Soft Law Ascends

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

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

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

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

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

Building on Past Governance Frameworks

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

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

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

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

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

Flexible, Adaptive Governance is Key

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

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

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

Addressing Likely Objections from Left & Right

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

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

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

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

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

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

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

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

Conclusion

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

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

Today, the U.S. Department of Transportation released its eagerly-awaited “Federal Automated Vehicles Policy.” There’s a lot to like about the guidance document, beginning with the agency’s genuine embrace of the potential for highly automated vehicles (HAVs) to revolutionize this sector and save thousands of lives annually in the process.

It is important we get HAV policy right, the DOT notes, because, “35,092 people died on U.S. roadways in 2015 alone” and “94 percent of crashes can be tied to a human choice or error.” (p. 5) HAVs could help us reverse that trend and save thousands of lives and billions in economic costs annually. The agency also documents many other benefits associated with HAVs, such as increasing personal mobility, reducing traffic and pollution, and cutting infrastructure costs.

I will not attempt here to comment on every specific recommendation or guideline suggested in the new DOT guidance document. I could nit-pick about some of the specific recommended guidelines, but I think many of the guidelines are quite reasonable, whether they are related to safety, security, privacy, or state regulatory issues. Other issues need to be addressed and CEI’s Marc Scribner does a nice job documenting some of them is his response to the new guidelines.

Instead of discussing those specific issues today, I want to ask a more fundamental and far-reaching question which I have been writing about in recent papers and essays: Is this guidance or regulation? And what does the use of informal guidance mechanisms like these signal for the future of technological governance more generally?

When Is “Voluntary” Really Mandatory?

The surreal thing about DOT’s new driverless car guidance is how the agency repeatedly stresses it “is not mandatory” and that the guidelines are voluntary in nature but then — often in the same paragraph or sentence — the agency hints how it might convert those recommendations into regulations in the near future. Consider this paragraph on pg. 11 of the DOT’s new guidance document:

The Agency expects to pursue follow-on actions to this Guidance, such as performing additional research in areas such as benefits assessment, human factors, cybersecurity, performance metrics, objective testing, and others as they are identified in the future. As discussed, DOT further intends to hold public workshops and obtain public comment on this Guidance and the other elements of the Policy. This Guidance highlights important areas that manufacturers and other entities designing HAV systems should be considering and addressing as they design, test, and deploy HAVs. This Guidance is not mandatory. NHTSA may consider, in the future, proposing to make some elements of this Guidance mandatory and binding through future regulatory actions. This Guidance is not intended for States to codify as legal requirements for the development, design, manufacture, testing, and operation of automated vehicles. Additional next steps are outlined at the end of this Guidance. [emphasis added.]

The agency continues on to request that “manufacturers and other entities voluntarily provide reports regarding how the Guidance has been followed,” but then notes how “[t]his reporting process may be refined and made mandatory through a future rulemaking.” (p. 15)

And so it goes throughout the DOT’s new “guidance” document. With one breath the DOT suggests that everything is informal and voluntary; with the next it suggests that some form of regulation could be right around the proverbial corner.

Agency Threats Are the Future of Technological Governance

What’s going on here? In essence, DOT’s driverless car guidance is another example of how “soft law” and “agency threats” are becoming the dominant governance models for fast-paced emerging technology.

As noted by Tim Wu, a proponent of such regimes, these agency threats can include “warning letters, official speeches, interpretations, and private meetings with regulated parties.” “Soft law” simply refers to any sort of informal governance mechanism that agencies might seek to use to influence private decision-making or in this case the future course of technological innovation.

The problem with agency threats, as my former Mercatus Center colleague Jerry Brito pointed out in a 2014 law review article, is that they are fundamentally undemocratic and represent a betrayal of the rule of law. The use of “threat regimes,” Brito argued, “places undue power in the hands of regulators unconstrained by predictable procedures.” Such regimes breed uncertainty by leaving decisions up to the whim of regulators who will be unconstrained by administrative procedures, legal precedents, and strict timetables. “[B]ecause it has no limiting principle,” Brito concluded, the agency threats model “leaves the regulatory process without much meaning” and “would obviously be ripe for abuse.”

The danger exists that we are witnessing gradual mission creep as the DOT’s “guidance” process slowly moves from being a truly voluntary self-certification process to something more akin to a pre-market approval process. Every “informal” request that DOT makes — even when those requests are just presented in the form of vague questions — opens the door to greater technocratic meddling in the innovation process by federal bureaucrats.

Coping with the Pacing Problem

Why are agencies like the DOT adopting this new playbook? In a nutshell, it comes down to the realization on their part that the “pacing problem” is now an undeniable fact of life.

I discussed the pacing problem at length in my recent review of Wendell Wallach’s important new book, A Dangerous Master: How to Keep Technology from Slipping beyond Our Control. Wallach nicely defined the pacing problem as “the gap between the introduction of a new technology and the establishment of laws, regulations, and oversight mechanisms for shaping its safe development.” “There has always been a pacing problem,” Wallach noted, but like other philosophers, he believes that modern technological innovation is occurring at an unprecedented pace, making it harder than ever to “govern” it using traditional legal and regulatory mechanisms.

Which is exactly why the DOT and whole lot of other agencies are now defaulting to soft law and agencies threat models as their old regimes struggle to keep up with the pace of modern technological innovation. As the DOT put it in its new guidance document: “The speed with which HAVs are advancing, combined with the complexity and novelty of these innovations, threatens to outpace the Agency’s conventional regulatory processes and capabilities.” (p. 8)  More specifically, the agency notes that:

The remarkable speed with which increasingly complex HAVs are evolving challenges DOT to take new approaches that ensure these technologies are safely introduced (i.e., do not introduce significant new safety risks), provide safety benefits today, and achieve their full safety potential in the future. To meet this challenge, we must rapidly build our expertise and knowledge to keep pace with developments, expand our regulatory capability, and increase our speed of execution. (p. 6)

Rarely has any agency been quite so blunt about how it is racing to get ahead of the pacing problem before it completely loses control of the future course of technological innovation.

But the DOT is hardly alone in its increased reliance on soft law governance mechanisms. In fact, I’m in the early research stages of a new paper about what soft law and agency threat models mean for the future of emerging technology and its governance. In that paper, I hope to document how many different agencies (FAA, FDA, FTC, FCC, NTIA, & DOT among others) are using some variant of soft law model to informally regulate the growing universe of emerging technologies out there today (commercial drones, connected medical devices, the Internet of Things, 3D printing, immersive technology, the sharing economy, driverless cars, and more.)

If nothing else, I would like to devise a taxonomy of soft law/agency threat models and then discuss the upsides and downsides of those models. If anyone has recommendations for additional reading on this topic, please let me know. The best thing I have seen on the issue is a 2013 book of collected essays on Innovative Governance Models for Emerging Technologies, edited by Gary E. Marchant, Kenneth W. Abbott and Braden Allenby. I’m surprised more hasn’t been written about this in law reviews or political science journals.

What Does It Mean for Innovation? And Accountable Government?

So, what does all this mean for the future of driverless cars, autonomous systems, and other emerging technologies? I think it’s both good and bad news.

The good news — at least from the perspective of those of us who want to see innovators freed up to experiment more without prior restraint — is that the technological genie is increasingly out of the bottle. Technology regulators are at an impasse and they know it. Their old regulatory regimes are doomed to always be one step behind the action. Thus, a lot of technological innovation is going to be happening before any blessing has been given to engage in those experiments.

The bad news is that the regulatory regimes of the future will become almost hopelessly arbitrary in terms of their contours and enforcement ceiling. Basically, in our new world of soft law and agency threats, you can tear up the Administrative Procedures Act and throw it out the window.  When regulatory agencies act in the future, they will do so in a sort of extra-legal Twilight Zone, where things are not always as they seem. Agencies will increasingly act like nagging nannies, constantly pressuring innovators to behave themselves. And sometimes that nagging will work, and sometimes it will even improve consumer welfare at the margin! It will work sometimes precisely because government still wields a mighty big hammer and no innovator wants to be nailed to the ground in the courts, or the court of public opinion for that matter. Thus, many — not all, but many — of those innovators will go along with whatever agencies like DOT suggests as “best practices” even if those guidelines are horribly misguided or have no force of law whatsoever. And because agencies know that many (perhaps most) innovators will fall in line with whatever “best practices” or “codes of conduct” that they concoct, it will reinforce the legitimacy of this model and become the new method of imposing their will on current or emerging technology sectors.

Again, agency threats won’t always work because some innovators will continue to engage in rough forms of “technological civil disobedience” and just ignore a lot of these informal guidelines and agency threats. Agencies will push back and seek to make an example of specific innovators (especially the ones with deep pockets) in order to send a message to every other innovator out there that they better fall in line or else!

But what that “or else!” moment or action looks like remains completely unclear. The problem with soft law is that, by its very nature, it is completely open-ended and fundamentally arbitrary. It is really just “ non-law law.” That’s the “legal regime” that will “govern” the emerging technologies of the present and the future.

Isn’t Soft Law Better Than the Alternative?

Now, here’s the funny thing about this messy, arbitrary, unaccountable world of soft law and agency threats: It is probably a hell of lot better than the old world we used to live in!

The old analog era regulatory systems were very top-down and command-and-control in orientation. These traditional regimes were driven by the desire of regulators to enforce policy priorities by imposing prior restraints on innovation and then selectively passing out permission slips to get around those rules.

As I noted in my latest book, the problem with those traditional regulatory systems is that they “tend to 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.” (Permissionless Innovation, p. 120)

For all the reasons I outlined in my book and other papers on these topics, “permissionless innovation” remains the superior policy default compared to precautionary principle-based prior restraints. But I am not so naïve as to expect that permissionless innovation will prevail in the policy world all of the time. Moreover, I am not one of those technological determinists who goes around saying that technology is an unstoppable force that relentlessly drives history, regardless of what policymakers say. I am more of a soft determinist who believes that technology often can be a major driver of history, but not without a significant shaping from other social, cultural, economic, and political forces.

Thus, as much as I worry about the new “soft law/agency threats” regime being arbitrary, unaccountable, and innovation-threatening, I know that the ideal of permissionless innovation will only rarely be our default policy regime. But I also don’t think we are going back the old regulatory regimes of the past and we absolutely wouldn’t want to anyway in light of the deleterious impacts those regimes had on innovation in practice.

The best bet for those of us who care about the freedom to innovate is to make sure that these soft law governance mechanisms have some oversight from Congress (unlikely) and the Courts (more likely) when agencies push too far with informal agency threats. Better yet, we can hope that the pace of technological change continues to accelerate and pressures agencies to only intervene to address the most pressing problems and then largely leaves the rest of the field wide open for continued experimentation with new and better ways of doing things.

But make no doubt about it, as today’s DOT guidance document for driverless cars makes clear, “agency threats” will increasingly shape the future of emerging technologies whether we like it or not.

[UPDATE 4/30/13: This article was subsequently published in Volume 65, Issues 2 of the Federal Communications Law Journal in April 2013. The links below now point to the final FCLJ version.]

The Mercatus Center at George Mason University has just released a new paper by Brent Skorup and me entitled, “Uncreative Destruction: The War on Vertical Integration in the Information Economy.”  Brent, who is the research director for the Information Economy Project at the George Mason University School of Law, and I have been working on this paper since the Spring and we are looking forward to getting it published in a law review shortly. The paper focuses on Tim Wu’s “separations principle” for the digital economy, something I’ve spent some time critiquing here in the past. Here’s the introduction from the 44-page paper that Brent and I just released:

Are information sectors sufficiently different from other sectors of the economy such that more stringent antitrust standards should be applied to them preemptively? Columbia Law School professor Tim Wu responds in the affirmative in his book The Master Switch: The Rise and Fall of Information Empires. Having successfully pushed net-neutrality regulation into the policy spotlight, Wu has turned his attention to what he regards as excessive market concentration and threats to free speech throughout the entire information economy.To support his call for increased antitrust intervention, Wu explains his view of competition in the information economy—a view that deviates substantially from current mainstream antitrust theory. First, Wu contends that “information monopolies” are pervasive in the information economy. Wu’s “monopolists” include Facebook, Apple, Google, and even Twitter. In The Master Switch and essays like “In the Grip of the New Monopolists,” Wu argues that these so-called monopolies are increasing their market power and require more aggressive oversight and regulation.Second, Wu argues that traditional antitrust analysis is not sufficient for information systems because they carry speech. He claims, “Information industries… can never be properly understood as ‘normal’ industries,”and traditional forms of regulation, including antitrust enforcement, “are clearly inadequate for the regulation of information industries.”Wu believes that because information industries “traffic in forms of individual expression” and are “fundamental to democracy,” they should be subject to greater regulatory treatment.Third, in contrast to current competition law’s focus on horizontal relationships, Wu desires a reinvigorated regulatory enforcement that addresses “the corrupting effects of vertically integrated power” in the information sectors.He is particularly concerned about private threats to free speech arising from such vertical integration.The solution, he says, is preventing vertical mergers in the information economy and the mandatory divestiture of vertically integrated companies. To implement this, Wu proposes a Separations Principle for the information economy, which would segregate information providers into three buckets, which we have labeled information creators, information distributors, and hardware makers.This article outlines Wu’s separations proposal, explains why his fears regarding vertical relationships should be rejected by regulatory and antitrust policymakers, and illustrates the legal and practical problems his Separations Principle poses. Wu justifies his Separations Principle by citing monopolies and market power in the information economy. He also advocates using U.S. antitrust authorities to enforce his Principle. We argue that the antitrust harms he fears are not present, and we highlight scholarship on the accepted benefits of vertically integrated firms. We show that Wu’s remedies are policy preferences wrapped in the language of competition law. In fact, the information economy is largely competitive and does not warrant interventionist regulatory enforcement. Since much of American economic vitality flows from the information economy and technology, policymakers should reject a radical antitrust remedy like Wu’s preemptive Separations Principle.

The paper can be downloaded from the Mercatus website, SSRN, or Scribd.

Ahoy, TLFers!  You’ll notice that we’ve incorporated a new comment management system on the blog:  Pronounced “discuss” (not “discus” as one might well assume–a potential branding problem indeed for an otherwise promising start-up), Disqus has exploded in the last few months (Google Trends) to over 30,000 blogs.

Disqus should help the TLF become even more of a true community–in which comments can be as valuable as blog pieces themselves and in which the line between “reader” and “author” is further blurred.  Here‘s a list of cool things Disqus will let you, TLF’s valued readers to do:

Track and manage comments and replies

More control over your own comments on websites

Never lose your comments, even if the website goes away

Build a global profile, or comment blog, to collect and show off what you’re saying

Easier to comment on websites using Disqus

Reply to comments through email or mobile

Edit and republish comments with one click

In particular, comments can now be directed as replies to other comments, creating clear discussion threads.

You might be wondering:  “If Disqus is so darn awesome, why haven’t we incorporated it before?”  The answer is that, until the new Disqus plug-in for WordPress came out a few days ago, comments were stored only on the Disqus site and merely replicated on partner blogs–making comments unsearchable, among other things.  Now, we get the best of both worlds:  Comments will beseemlessly duplicated and synchronized between our database and Disqus’s.

While it will still be possible to comment on the blog just as before (anonymously or merely without a Disqus account), we do encourage readers to take a minute (literally) to set up a free Disqus account.  (For those of you who enjoy reading Terms of Use and Privacy policies or who just stay up late at night clutching their now-constitutionally-protected firearms and worrying about being tagged, tracked and someday unceremoniously culled from the herd, here are Disqus’s policies.)  For the less privacy-obsessed, here‘s a general FAQ about Discus.

There are a number of bells and whistles you can enable–like tying your Disqus account to other social networking sites and adding a small image of yourself (or some other hopefully-family-friendly image).  But the one important thing everyone who has posted comments in the past should do is to “claim” your old comments by entering the email address associated with those comments on Disqus. (You’ll get a verification email at that address.)  We would particularly encourage other bloggers who read the TLF to consider adding Disqus to their own blogs to take full advantage of Disqus as a platform for carrying on discussions across multiple blogs.

Please let us know by commenting here if you notice any quirks about the conversion, such as comments not carrying over from the old system or comments you want to claim as your own but can’t.