AI Policy Restrictions - Two Views

Among the many unique experiences of reporting on A.I. is this: In a young industry flooded with hype and money, person after person tells me that they are desperate to be regulated, even if it slows them down. In fact, especially if it slows them down.

What they tell me is obvious to anyone watching. Competition is forcing them to go too fast and cut too many corners. This technology is too important to be left to a race between Microsoft, Google, Meta and a few other firms. But no one company can slow down to a safe pace without risking irrelevancy. That’s where the government comes in — or so they hope.

A place to start is with the frameworks policymakers have already put forward to govern A.I. The two major proposals, at least in the West, are the “Blueprint for an A.I. Bill of Rights,” which the White House put forward in 2022, and the Artificial Intelligence Act, which the European Commission proposed in 2021. Then, last week, China released its latest regulatory approach. (...)

[ed. Generally broad explanations of each follow...]

This is not meant to be an exhaustive list. Others will have different priorities and different views. And the good news is that new proposals are being released almost daily. The Future of Life Institute’s policy recommendations are strong, and I think the A.I. Objectives Institute’s focus on the human-run institutions that will design and own A.I. systems is critical. But one thing regulators shouldn’t fear is imperfect rules that slow a young industry. For once, much of that industry is desperate for someone to help slow it down.

The Surprising Thing A.I. Engineers Will Tell You if You Let Them (by Ezra Klein, NY Times)

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Our societal experience with self-driving technology reminds me of the saying, “Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years.” It’s a saying that everybody should keep squarely in mind when thinking and forecasting about generative AI, as well. Even with a fundamental technological breakthrough, there always needs to be further (and unpredictable) innovation in the technology itself, complementary innovations, regulatory responses, and efficient business adoption. And while many of these things seem to be happening at warp speed right now with GenAI, especially large language models such as ChatGPT, history suggests the process will take far longer than all the breathless tweeting currently indicates. (...)

The bottleneck is not the technology – though faster advances certainly wouldn’t hurt – but rather a lack of complementary process innovation, workforce reskilling and business dynamism. Simply plugging in new technologies without changing business organization and workforce skills is like paving the cow paths. It leaves the real benefits largely untapped. However, by making complementary investments, we can speed up productivity growth.

Almost certainly new regulations will be part of that process, as the recent calls for a pause in training large language models suggest. And as those calls show, there will be ideas both good and bad. To avoid creating a regulatory bottleneck, I urge policymakers to take a look at a new analysis by Adam Thierer of R Street, “Getting AI Innovation Culture Right.” Among things, Thierer argues that the U.S. should foster the development of AI, machine learning and robotics by following the policy vision that drove the digital revolution.

It’s a policy vision based on the President Bill Clinton-era Framework for Global Electronic Commerce, which contained five key principles: (1) private sector leadership and market-driven innovation, (2) minimal government regulation and intervention, (3) a predictable, minimalist, consistent and simple legal environment for commerce, (4) recognition of the unique qualities and decentralized nature of the Internet, and (5) facilitation of global electronic commerce and consistent legal framework. These principles can guide technology policy today. just as effectively as they did back then.

As policymakers consider governance solutions for AI and computational systems, they should appreciate how a policy paradigm that stacks the deck against innovation by default will get significantly less innovation as a result. Innovation culture is a function of incentives, and policy incentives can influence technological progress both directly and indirectly. Over the last half century, “regulation has clobbered the learning curve” for many important technologies in the United States in a direct way, especially those in the nuclear, nanotech and advanced aviation sectors.106 Society has missed out on many important innovations because of endless foot-dragging or outright opposition to change from special interests, anti-innovation activists and over-zealous bureaucrats.

What self-driving cars should teach us about generative AI (by James Pethokoukis, Faster, Please!)

Image: Matt Edge for The New York Times

[ed. Personally, I lean more toward the former. If you're dealing with an existential threat, pace of business innovation should probably take a back seat.]