New Report: Do We Need Global Government to Address AI Risk?

by on June 16, 2023 · 0 comments

Can we advance AI safety without new international regulatory bureaucracies, licensing schemes or global surveillance systems? I explore that question in my latest R Street Institute study, “Existential Risks & Global Governance Issues around AI & Robotics.” (31 pgs)  My report rejects extremist thinking about AI arms control & stresses how the “realpolitik” of international AI governance is such that things cannot and must not be solved through silver-bullet gimmicks and grandiose global government regulatory regimes.

The report uses Nick Bostrom’s “vulnerable world hypothesis” as a launching point and discusses how his five specific control mechanisms for addressing AI risks have started having real-world influence with extreme regulatory proposals now being floated. My report also does a deep dive into the debate about a proposed global ban on “killer robots” and looks at how past treaties and arms control efforts might apply, or what we can learn from them about what won’t work.

I argue that proposals to impose global controls on AI through a worldwide regulatory authority are both unwise and unlikely to work. Calls for bans or “pauses” on AI developments are largely futile because many nations will not agree to them. As with nuclear and chemical weapons, treaties, accords, sanctions and other multilateral agreements can help address some threats of malicious uses of AI or robotics. But trade-offs are inevitable, and addressing one type of existential risk sometimes can give rise to other risks.

A culture of AI safety by design is critical. But there is an equally compelling interest in ensuring algorithmic innovations are developed and made widely available to society. The most effective solution to technological problems usually lies in more innovation, not less. Many other multistakeholder and multilateral efforts can help AI safety. Final third of my study is devoted to a discussion of that. Continuous communication, coordination, and cooperation—among countries, developers, professional bodies and other stakeholders—will be essential. Continue reading →

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Podcast: “Who’s Afraid of Artificial Intelligence?”

by on June 12, 2023 · 0 comments

This week, I appeared on the Tech Freedom Tech Policy Podcast to discuss “Who’s Afraid of Artificial Intelligence?” It’s an in-depth, wide-ranging conversation about all things AI related. Here’s a summary of what host what Corbin Barthold and I discussed:

1. The “little miracles happening every day” thanks to AI

2. Is AI a “born free” technology?

3. Potential anti-competitive effects of AI regulation

4. The flurry of joint letters

5. new AI regulatory agency political realities

6. the EU’s Precautionary Principle tech policy disaster

7. The looming “war on computation” & open source

8. The role of common law for AI

9. Is Sam Altman breaking the very laws he proposes?

10. Do we need an IAEA for AI or an “AI Island”

11. Nick Bostrom’s global control & surveillance model

12. Why “doom porn” dominates in academic circles

13. Will AI take all the jobs?

14. Smart regulation of algorithmic technology

15. How the “pacing problem” is sometimes the “pacing benefit”

 

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Podcast: “Artificial Intelligence for Dummies”

by on June 12, 2023 · 0 comments

It was my pleasure to recently appear on the Independent Women’s Forum’s “She Thinks” podcast to discuss “Artificial Intelligence for Dummies.” In this 24-minute conversation with host Beverly Hallberg, I outline basic definitions, identify potential benefits, and then consider some of the risks associated with AI, machine learning, and algorithmic systems.

Reminder, you can find all my relevant past work on these issues via my, “Running List of My Research on AI, ML & Robotics Policy.”

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event video: “Does the US Need a New AI Regulator?”

by on June 7, 2023 · 0 comments

Here’s the video from a June 6th event on, “Does the US Need a New AI Regulator?” which was co-hosted by Center for Data Innovation & R Street Institute. We discuss algorithmic audits, AI licensing, an “FDA for algorithms” and other possible regulatory approaches, as well as various “soft law” self-regulatory efforts and targeted agency efforts. The event was hosted by Daniel Castro and included Lee Tiedrich, Shane Tews, Ben Shneiderman and me.

Continue reading →

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Podcast: Should We Regulate AI?

by on May 8, 2023 · 0 comments

It was my pleasure to recently join Matthew Lesh, Director of Public Policy and Communications for the London-based Institute of Economic Affairs (IEA), for the IEA podcast discussion, “Should We Regulate AI?” In our wide-ranging 30-minute conversation, we discuss how artificial intelligence policy is playing out across nations and I explained why I feel the UK has positioned itself smartly relative to the US & EU on AI policy. I argued that the UK approach encourages a better ‘innovation culture’ than the new US model being formulated by the Biden Administration.

We also went through some of the many concerns driving calls to regulate AI today, including: fears about job dislocations, privacy and security issues, national security and existential risks, and much more.

Continue reading →

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Finally: Clearer FAA Guidance on State and Local Airspace Restrictions

by on May 7, 2023 · 0 comments

I stumbled across a surprising drone policy update in the FAA’s Aeronautical Information Manual (Manual) last week. The Manual contains official guidance and best practices to US airspace users. (My friend Marc Scribner reminds me that the Manual is not formally regulatory, though it often restates or summarizes regulations.) The manual has a (apparently) new section: “Airspace Access for UAS.” In subsection “Airspace Restrictions To Flight” (11-4-6) it notes:

There can be certain local restrictions to airspace. While the FAA is designated by federal law to be the regulator of the NAS [national airspace system], some state and local authorities may also restrict access to local airspace. UAS pilots should be aware of these local rules.

Legally speaking, the FAA is recognizing there is no “field preemption” when it comes to low-altitude airspace restrictions. In sharing this provision around with aviation and drone experts, each agreed this was a new and surprising policy guidance. The drone provisions appear to have been part of updates made on April 20, 2023. In my view, it’s very welcome guidance.

Some background: In 2015, the FAA released helpful “fact sheet” to state and local officials about drone regulations, as state legislatures began regulating drone operations in earnest. The FAA noted the several drone-related areas, including aviation safety, where federal aviation rules are extensive. The agency noted:

Laws traditionally related to state and local police power – including land use, zoning, privacy,
trespass, and law enforcement operations – generally are not subject to federal regulation.

To ensure state and federal drone laws were not in conflict, the FAA recommended that state and local officials consult with the FAA before creating “operational UAS restrictions on flight altitude, flight paths; operational bans; any regulation of the navigable airspace.”

That guidance is still current and still useful. Around 2017, however, it seems some within the FAA began publicly and privately taking a rather harder line regarding state and local rules about drone operations. For instance, in July 2018, someone at the FAA posted a confusing and brief new statement on the FAA website about state and local drone rules that is hard to reconcile with the 2015 guidance. Continue reading →

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My Latest Study on AI Governance

by on April 20, 2023 · 0 comments

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.

Continue reading →

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On “Pausing” AI

by on April 7, 2023 · 0 comments

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

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

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

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

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

The editors continue on to rightly note:

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

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

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What Policy Vision for Artificial Intelligence?

by on April 2, 2023 · 0 comments

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. Continue reading →

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Why Isn’t Everyone Already Unemployed Due to Automation?

by on March 11, 2023 · 0 comments

I have a new R Street Institute policy study out this week doing a deep dive into the question: “Can We Predict the Jobs and Skills Needed for the AI Era?” There’s lots of hand-wringing going on today about AI and the future of employment, but that’s really nothing new. In fact, in light of past automation panics, we might want to step back and ask: Why isn’t everyone already unemployed due to technological innovation?

To get my answers, please read the paper! In the meantime, here’s the executive summary:

To better plan for the economy of the future, many academics and policymakers regularly attempt to forecast the jobs and worker skills that will be needed going forward. Driving these efforts are fears about how technological automation might disrupt workers, skills, professions, firms and entire industrial sectors. The continued growth of artificial intelligence (AI), robotics and other computational technologies exacerbate these anxieties.

Yet the limits of both our collective knowledge and our individual imaginations constrain well-intentioned efforts to plan for the workforce of the future. Past attempts to assist workers or industries have often failed for various reasons. However, dystopian predictions about mass technological unemployment persist, as do retraining or reskilling programs that typically fail to produce much of value for workers or society. As public efforts to assist or train workers move from general to more specific, the potential for policy missteps grows greater. While transitional-support mechanisms can help alleviate some of the pain associated with fast-moving technological disruption, the most important thing policymakers can do is clear away barriers to economic dynamism and new opportunities for workers.

I do discuss some things that government can do to address automation fears at the end of the paper, but it’s important that policymakers first understand all the mistakes we’ve made with past retraining and reskilling efforts. The easiest thing to do to help in the short-term is clear away barriers to labor mobility and economic dynamism, I argue. Again, read the study for details.

For more info on other AI policy developments, check out my running list of research on AI, ML robotics policy.

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