Questions swirl around AI regulation

Among the many unique experiences of reporting on artificial intelligence 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 among Microsoft, Google, Meta and a few other firms. But no 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 AI. The two major proposals, at least in the West, are the Blueprint for an AI 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.

Let’s start with the European proposal, as it came first. The AIA tries to regulate AI systems according to how they’re used. It is particularly concerned with high-risk uses, which include everything from overseeing critical infrastructure to grading papers to calculating credit scores to making hiring decisions. High-risk uses, in other words, are any use in which a person’s life or livelihood might depend on a decision made by a machine-learning algorithm.

The European Commission described this approach as “future-proof”, which proved to be predictably arrogant, as new AI systems have already thrown the bill’s clean definitions into chaos. Focusing on use cases is fine for narrow systems designed for a specific use, but it’s a category error when it’s applied to generalised systems. Models such as GPT-4 don’t do any one thing except predict the next word in a sequence. You can use them to write code, pass the bar exam, draw up contracts, create political campaigns, plot market strategy, and power AI companions or sexbots. In trying to regulate systems by use case, the AIA ends up saying very little about how to regulate the underlying model that’s powering all these use cases.

Unintended consequences abound. The AIA mandates, for example, that in high-risk cases, “training, validation and testing data sets shall be relevant, representative, free of errors and complete.” But what the large language models are showing is that the most powerful systems are those trained on the largest data sets. Those sets can’t plausibly be free of error, and it’s not clear what it would mean for them to be “representative.” There’s a strong case to be made for data transparency, but I don’t think Europe intends to deploy weaker, less capable systems across everything from exam grading to infrastructure.

The other problem with the use-case approach is that it treats AI as a technology that will, itself, respect boundaries. But its disrespect for boundaries is what most worries the people working on these systems. Imagine that “personal assistant” is rated as a low-risk use case and a hypothetical GPT-6 is deployed to power an absolutely fabulous personal assistant. The system gets tuned to be extremely good at interacting with humans and accomplishing a diverse set of goals in the real world. That’s great until someone asks it to secure a restaurant reservation at the hottest place in town and the system decides that the only way to do it is to cause a disruption that leads one-third of that night’s diners to cancel their bookings.

Sounds like sci-fi? Sorry, but this kind of problem is sci-fact.

The White House’s Blueprint for an AI Bill of Rights is a more interesting proposal. But where the European Commission’s approach is much too tailored, the White House blueprint may well be too broad. No AI system today comes close to adhering to the framework, and it’s not clear that any of them could.

The blueprint says that “automated systems should be developed with consultation from diverse communities, stakeholders and domain experts to identify concerns, risks and potential impacts of the system.” This is crucial, and it would be interesting to see the White House or Congress flesh out how much consultation is needed, what type is sufficient and how regulators will make sure the public’s wishes are actually followed.

Perhaps the most interesting of the blueprint’s proposals is that “you should be able to opt out from automated systems in favour of a human alternative, where appropriate.” In that sentence, the devil lurks in the definition of “appropriate”. But the underlying principle is worth considering. Should there be an opt-out from AI systems? Which ones? When is an opt-out clause a genuine choice, and at what point does it become merely an invitation to recede from society altogether, like saying you can choose not to use the internet or vehicular transport or banking services if you so choose.

Then there are China’s proposed new rules. I won’t say much on these, except to note that they are much more restrictive than anything the United States or Europe is imagining, which makes me very sceptical of arguments that we are in a race with China to develop advanced artificial intelligence. China seems perfectly willing to cripple the development of general AI so it can concentrate on systems that will more reliably serve state interests.

This article originally appeared in The New York Times.