AI Experts Aren’t Always Right About AI
One of the lasting consequences of the Covid-19 pandemic has been a decline of trust in public-health experts and institutions. It is not hard to see why: America botched Covid testing, kept the schools closed for far too long, failed to vaccinate enough people quickly enough, and inflicted far more economic damage than was necessary — and through all this, public-health experts often had the dominant voice.
In their defense, public-health officials are trained to prioritize public safety above all else. And to their credit, many now recognize that any response to a public-health crisis needs to consider the tradeoffs inherent in any intervention. As Dr. Anthony Fauci recently told the New York Times, “I'm not an economist.”
As it happens, I am. And my fear is that we are about to make the same mistake again — that is, trusting the wrong experts — with artificial intelligence.
Some of the greatest minds in the field, such as Geoffrey Hinton, are speaking out against AI developments and calling for a pause in AI research. Earlier this week, Hinton left his AI work at Google, declaring that he was worried about misinformation, mass unemployment and future risks of a more destructive nature. Anecdotally, I know from talking to people working on the frontiers of AI, many other researchers are worried too.
What I do not hear, however, is a more systematic cost-benefit analysis of AI progress. Such an analysis would have to consider how AI might fend off other existential risks — deflecting that incoming asteroid, for example, or developing better remedies against climate change — or how AI could cure cancer or otherwise improve our health. And these analyses often fail to take into account the risks to America and the world if we
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