Think of Everything You Hate About the Internet. Now Add A.I.

“In 2021, I interviewed Ted Chiang, one of the great living sci-fi writers. Something he said to me then keeps coming to mind now.

“I tend to think that most fears about A.I. are best understood as fears about capitalism,” Chiang told me. “And I think that this is actually true of most fears of technology, too. Most of our fears or anxieties about technology are best understood as fears or anxiety about how capitalism will use technology against us. And technology and capitalism have been so closely intertwined that it’s hard to distinguish the two.” [..]

We are talking so much about the technology of A.I. that we are largely ignoring the business models that will power it. That’s been helped along by the fact that the splashy A.I. demos aren’t serving any particular business model, save the hype cycle that leads to gargantuan investments and acquisition offers. But these systems are expensive and shareholders get antsy. The age of free, fun demos will end, as it always does. Then, this technology will become what it needs to become to make money for the companies behind it, perhaps at the expense of its users. [..]

I spoke this week with Margaret Mitchell, who helped lead a team focused on A.I. ethics at Google — a team that collapsed after Google allegedly began censoring its work. These systems, she said, are terribly suited to being integrated into search engines. “They’re not trained to predict facts,” she told me. “They’re essentially trained to make up things that look like facts.”

So why are they ending up in search first? Because there are gobs of money to be made in search. Microsoft, which desperately wanted someone, anyone, to talk about Bing search, had reason to rush the technology into ill-advised early release. “The application to search in particular demonstrates a lack of imagination and understanding about how this technology can be useful,” Mitchell said, “and instead just shoehorning the technology into what tech companies make the most money from: ads.” [..]

What is advertising, at its core? It’s persuasion and manipulation. In his book “Subprime Attention Crisis,” Tim Hwang, a former director of the Harvard-M.I.T. Ethics and Governance of A.I. Initiative, argues that the dark secret of the digital advertising industry is that the ads mostly don’t work. His worry, there, is what happens when there’s a reckoning with their failures. [..]

What about when these systems are deployed on behalf of the scams that have always populated the internet? How about on behalf of political campaigns? Foreign governments? “I think we wind up very fast in a world where we just don’t know what to trust anymore,” Gary Marcus, the A.I. researcher and critic, told me. “I think that’s already been a problem for society over the last, let’s say, decade. And I think it’s just going to get worse and worse.” [..]

There are business models that might bring these products into closer alignment with users. I’d feel better, for instance, about an A.I. helper I paid a monthly fee to use rather than one that appeared to be free, but sold my data and manipulated my behavior. But I don’t think this can be left purely to the market. It’s possible, for example, that the advertising-based models could gather so much more data to train the systems that they’d have an innate advantage over the subscription models, no matter how much worse their societal consequences were.

There is nothing new about alignment problems. They’ve been a feature of capitalism — and of human life — forever. Much of the work of the modern state is applying the values of society to the workings of markets, so that the latter serve, to some rough extent, the former. We have done this extremely well in some markets — think of how few airplanes crash, and how free of contamination most food is — and catastrophically poorly in others.

One danger here is that a political system that knows itself to be technologically ignorant will be cowed into taking too much of a wait-and-see approach to A.I. There is a wisdom to that, but wait long enough and the winners of the A.I. gold rush will have the capital and user base to resist any real attempt at regulation. Somehow, society is going to have to figure out what it’s comfortable having A.I. doing, and what A.I. should not be permitted to try, before it is too late to make those decisions.

I might, for that reason, alter Chiang’s comment one more time: Most fears about capitalism are best understood as fears about our inability to regulate capitalism.”

Full editorial, E Klein, New York Times, 2023.2.26