This sounds like a loaded question but... Why don't more people who talk about AI $subject (eg, ethics, policy, strategy) actually engage with foundational technical literature? What is the plausible justification for not habitually skimming Arxiv?
Because they can do well professionally without. As an ethnographic adventure I went to a conference last year focused on AI, UBI etc. I didn't meet anyone, or see a single speaker, who was engaged w the primary literature on AI. But lots of these ppl speak at Davos, in NYT etc
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See also: https://twitter.com/kristineberth/status/997356628339904514 … It's really interesting, and I'll bet there's a good book in it.
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An amusing test reporters could apply: "Oh, before we begin, would you mind quickly writing down the equations describing backprop in, say, a simple feedforward net? Okay if it's a bit rough!" AKA, do you know how any of this works?
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Slightly unfair - there are people with interesting things to say who would fail this test. Still, I think the idea has merit, some kind of FizzBuzz for AI "experts".
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Like music, there are people who compose AI, people who play it, and people who listen to it. I think it's OK for music critics to not know how to compose or play
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Requiring people to know the specific mechanics is short sided. A lot of policy makers and analysts in any given field are divorced from the underlying techniques. I wouldn't expect an auto policy maker to think a lot about 4 cylinders vs 6, but more "cars and the climate".
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