Senior Fellow in Technology & Innovation at the R Street Institute in Washington, DC. Formerly a senior research fellow at the Mercatus Center at George Mason University, President of the Progress & Freedom Foundation, Director of Telecommunications Studies at the Cato Institute, and a Fellow in Economic Policy at the Heritage Foundation.
The Brookings Institution hosted this excellent event on frontier AI regulation this week featuring a panel discussion I was on that followed opening remarks from Rep. Ted Lieu (D-CA). I come in around the 51-min mark of the event video and explain why I worry that AI policy now threatens to devolve into an all-out war on computation and open source innovation in particular.
I argue that some pundits and policymakers appear to be on the way to substituting a very real existential risk (authoritarian govt control over computation/science) for a hypothetic existential risk of powerful AGI. I explain how there are better, less destructive ways to address frontier AI concerns than the highly repressive approaches currently being considered.
I have developed these themes and arguments at much greater length in a series of essays over on Medium over the past few months. If you care to read more, the four key articles to begin with are:
As always, I’ll have much more to say on this topic as the war on computation expands. This is quickly becoming the most epic technology policy battle of modern times.
I was my pleasure to participate in this Cato Institute event today on “Who’s Leading on AI Policy?
Examining EU and U.S. Policy Proposals and the Future of AI.” Cato’s Jennifer Huddleston hosted and also participating was Boniface de Champris, Policy Manager with the Computer and Communications Industry Association. Here’s a brief outline of some of the issues we discussed:
What are the 7 leading concerns driving AI policy today?
What is the difference between horizontal vs. vertical AI regulation?
Which agencies are moving currently to extend their reach and regulate AI tech?
What’s going on at the state, local, and municipal level in the US on AI policy?
How will the so-called “Brussels Effect” influence the course of AI policy in the US?
What have the results been of the EU’s experience with the GDPR?
How will the EU AI Act work in practice?
Can we make algorithmic systems perfectly transparent / “explainable”?
Should AI innovators be treated as ‘guilty until proven innocent’ of certain risks?
How will existing legal concepts and standards (like civil rights law and unfair and deceptive practices regulation) be applied to algorithmic technologies?
Do we have a fear-based model of AI governance currently? What role has science fiction played in fueling that?
What role will open source AI play going forward?
Is AI licensing a good idea? How would it even work?
Can AI help us identify and address societal bias and discrimination?
The New York Times today published my response to an oped by Senators Lindsey Graham & Elizabeth Warren calling for a new “Digital Consumer Protection Commission” to micromanage the high-tech information economy. “Their new technocratic digital regulator would do nothing but hobble America as we prepare for the next great global technological revolution,” I argue. Here’s my full response:
Senators Lindsey Graham and Elizabeth Warren propose a new federal mega-regulator for the digital economy that threatens to undermine America’s global technology standing.
A new “licensing and policing” authority would stall the continued growth of advanced technologies like artificial intelligence in America, leaving China and others to claw back crucial geopolitical strategic ground.
America’s digital technology sector enjoyed remarkable success over the past quarter-century — and provided vast investment and job growth — because the U.S. rejected the heavy-handed regulatory model of the analog era, which stifled innovation and competition.
The tech companies that Senators Graham and Warren cite (along with countless others) came about over the past quarter-century because we opened markets and rejected the monopoly-preserving regulatory regimes that had been captured by old players.
The U.S. has plenty of federal bureaucracies, and many already oversee the issues that the senators want addressed. Their new technocratic digital regulator would do nothing but hobble America as we prepare for the next great global technological revolution.
As I noted in a recent interview with James Pethokoukis for his Faster, Please! newsletter, “[t]he current policy debate over artificial intelligence is haunted by many mythologies and mistaken assumptions. The most problematic of these is the widespread belief that AI is completely ungoverned today.” In a recent R Street Institute report and series of other publications, I have documented just how wrong that particular assumption is.
The first thing I try to remind everyone is that the U.S. federal government is absolutely massive—2.1 million employees, 15 cabinet agencies, 50 independent federal commissions and 434 federal departments. Strangely, when policymakers and pundits deliver remarks on AI policy today, they seem to completely ignore all that regulatory capacity while simultaneously casually tossing out proposals to just add more and more layers of regulation and bureaucracy to it. Well, I say why not see if the existing regulations and bureaucracy are working first, and then we can have a chat about what more is needed to fill gaps.
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 →
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”
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.
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.
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.
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.
The Technology Liberation Front is the tech policy blog dedicated to keeping politicians' hands off the 'net and everything else related to technology. Learn more about TLF →
The Technology Liberation Front is the tech policy blog dedicated to keeping politicians' hands off the 'net and everything else related to technology.