I specifically highlight the danger of new measures from big states like NY and California, but it’s the patchwork of all the state and local regs that will result in a sort of ‘death-by-a-thousand-cuts’ for AI innovation as the red tape grows and hinders innovation and capital formation.
What we need is the same sort of principled, pro-innovation federal framework or AI that we adopted for the Internet a generation ago. Specifically, we need some sort of preemption of most of the state and local constraints on what is inherently national (and even global) commerce and speech.
Alas, Congress appears incapable of getting even basic things done on tech policy these days. Continue reading →
Here’s a new DC EKG podcast I recently appeared on to discuss the current state of policy development surrounding artificial intelligence. In our wide-ranging chat, we discussed:
why a sectoral approach to AI policy is superior to general purpose licensing
why comprehensive AI legislation will not pass in Congress
the best way to deal with algorithmic deception
why Europe lost its tech sector
how a global AI regulator threatens our safety
the problem with Biden’s AI executive order
will AI policy follow same path as nuclear policy?
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.
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:
The “little miracles happening every day” thanks to AI
Is AI a “born free” technology?
Potential anti-competitive effects of AI regulation
The flurry of joint letters
new AI regulatory agency political realities
the EU’s Precautionary Principle tech policy disaster
The looming “war on computation” & open source
The role of common law for AI
Is Sam Altman breaking the very laws he proposes?
Do we need an IAEA for AI or an “AI Island”
Nick Bostrom’s global control & surveillance model
Why “doom porn” dominates in academic circles
Will AI take all the jobs?
Smart regulation of algorithmic technology
How the “pacing problem” is sometimes the “pacing benefit”
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.
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 →
In my latest R Street Institute blog post, “Mapping the AI Policy Landscape Circa 2023: Seven Major Fault Lines,” I discuss the big issues confronting artificial intelligence and machine learning in the coming year and beyond. I note that the AI regulatory proposals are multiplying fast and coming in two general varieties: broad-based and targeted. Broad-based algorithmic regulation would address the use of these technologies in a holistic fashion across many sectors and concerns. By contrast, targeted algorithmic regulation looks to address specific AI applications or concerns. In the short-term, it is more likely that targeted or “sectoral” regulatory proposals have a chance of being implemented.
I go on to identify seven major issues of concern that will drive these policy proposals. They include:
In an earlier essay, I explored “Why the Future of AI Will Not Be Invented in Europe” and argued that, “there is no doubt that European competitiveness is suffering today and that excessive regulation plays a fairly significant role in causing it.” This essay summarizes some of the major academic literature that leads to that conclusion.
Since the mid-1990s, the European Union has been layering on highly restrictive policies governing online data collection and use. The most significant of the E.U.’s recent mandates is the 2018 General Data Protection Regulation (GDPR). This regulation established even more stringent rules related to the protection of personal data, the movement thereof, and limits what organizations can do with data. Data minimization is the major priority of this system, but there are many different types of restrictions and reporting requirements involved in the regulatory scheme. This policy framework also has ramifications for the future of next-generation technologies, especially artificial intelligence and machine learning systems, which rely on high-quality data sets to improve their efficacy.
Whether or not the E.U.’s complicated regulatory regime has actually resulted in truly meaningful privacy protections for European citizens relative to people in other countries remains open to debate. It is very difficult to measure and compare highly subjective values like privacy across countries and cultures. This makes benefit-cost analysis for privacy regulation extremely challenging — especially on the benefits side of the equation.
What is no longer up for debate, however, is the cost side of the equation and the question of what sort of consequences the GDPR has had on business formation, competition, investment, and so on. On these matters, standardized metrics exist and the economic evidence is abundantly clear: the GDPR has been a disaster for Europe. Continue reading →
I spent much of 2022 writing about the growing policy debate over artificial intelligence, machine learning, robotics, and the Computational Revolution more generally. Here are some of the major highlights of my work on this front.
POLICY DEFAULTS: “The Proper Governance Default for AI” – Which policy default should we choose for algorithmic technologies: The Precautionary Principle or The Proactionary Principle? This is the single most important issue in AI policy today.
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.