In the case of the AI, the Bayes net is explicit, in the sense that we could print it out on a sheet of paper and try to study it once training is done, and the main reason we don’t do that is because it’s likely to be too big to make much sense of.
We don’t quite have access to the AI Bayes net—we just have a big neural network, and we sometimes talk about examples where what the neural net is doing internally can be well-described as “inference in a Bayes net.”
So ideally a solution would use neither the human Bayes net or the AI Bayes net.
But when thinking about existing counterexamples, it can still be useful to talk about how we want an algorithm to behave in the case where the human/AI are using a Bayes net, and we do often think about ideas that use those Bayes nets (with the understanding that we’d ultimately need to refine them into approaches that don’t depend on having an explicit Bayes net).
We don’t quite have access to the AI Bayes net—we just have a big neural network, and we sometimes talk about examples where what the neural net is doing internally can be well-described as “inference in a Bayes net.”
So ideally a solution would use neither the human Bayes net or the AI Bayes net.
But when thinking about existing counterexamples, it can still be useful to talk about how we want an algorithm to behave in the case where the human/AI are using a Bayes net, and we do often think about ideas that use those Bayes nets (with the understanding that we’d ultimately need to refine them into approaches that don’t depend on having an explicit Bayes net).