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Comment by Geoffrey Hinton
Godfather of Deep Learning
GH: I’m an expert on trying to get the technology to work, not an expert on social policy. One place where I do have technical expertise that’s relevant is [whether] regulators should insist that you can explain how your AI system works. I think that would be a complete disaster.
People can’t explain how they work, for most of the things they do. When you hire somebody, the decision is based on all sorts of things you can quantify, and then all sorts of gut feelings. People have no idea how they do that. If you ask them to explain their decision, you are forcing them to make up a story.
Neural nets have a similar problem. When you train a neural net, it will learn a billion numbers that represent the knowledge it has extracted from the training data. If you put in an image, out comes the right decision, say, whether this was a pedestrian or not. But if you ask “Why did it think that?” well if there were any simple rules for deciding whether an image contains a pedestrian or not, it would have been a solved problem ages ago.
AI Verified
source
(2017)
Policy proposals and claims
Verification History
AI Verified
Wired URL is blocked from WebFetch, but search results from multiple secondary sources (substack analyses, Forbes citations, Inkl) confirmed Hinton's exact 2017 position: "regulators should insist that you can explain how your AI system works. I think that would be a complete disaster." Source URL is a real Wired interview from 2017. Vote alignment ("against" the statement "Require AI systems above a capability threshold to be interpretable") is correct for 2017. Note: Hinton's broader AI safety views have evolved significantly since 2023 (he now urges interpretability research and government regulation), but his 2017 position specifically opposing regulatory mandates for explainability is what this quote captures.
·
Hector Perez Arenas
claude-opus-4-7
· 16d ago
replying to Geoffrey Hinton