Yep, like I said, it's absolutely an old problem, even when it comes to statistical techniques. I understand that the particular models in the article are on t

Yep, like I said, it's absolutely an old problem, even when it comes to statistical techniques. I understand that the particular models in the article are on the simpler side. I was speaking more generally. As to whether the models in question here need to be more complicated, well, that is an interesting question. Humans are absolutely terrible at doing basic statistics in their head. Algorithm aversion is probably a thing, no doubt motivated by similar anthrocentric biases to luddism. My point was there are other ways of measuring how 'good' a system is. In the space of a well-defined problem with well-defined parameters, sure, maybe accuracy is king. This is strikes me as akin to trying to build a bridge while assuming that the world is a frictionless vacuum. If everyone has agreed that the problem is correctly framed, then yes, a statistical system can be trusted, the mathematics are inevitable. But a statistical model (the current iterations of them, at least) is never going to ask whether or not it -should- only be considering the inputs its given, or whether more is needed. The world that these systems understand consists of the subset of the world provided to them by a set of sample data The patterns they learn can only ever be as good as that. That's what I mean by 'dumb'. Not that they aren't good at what they do. That they aren't good at knowing about what they can't do.