Earlier quoted context omitted.
Her point is more than that. I think that she was trying to say that ML systems have an inherent bias that is independent of their data. This is a consequence of the fact that it's not possible to do generalization, and thus learning, without bias to begin with. What an AI researcher does when deciding which architecture is essentially tuning the bias in the generalization system in order to produce better results, w…
> Her point is more than that. I think that she was trying to say that ML systems have an inherent bias that is independent of their data. In general I take these types of tropes to be cloaked (politically correct) way of saying "fuck x group".
Dataset bias is only one way this occurs, though. Even if you include more colored faces, you still may be taking pictures with a camera that doesn't capture as much contrast. Or even if you retrain, you may have chosen model structure to optimize behavior in a biased dataset. Or... if you're considering the cost of misidentification by a facial recognition system used by, say, police, you need to be sure that you take a perspective that applies to society as a whole and not to your own interactions with police.
It's perfectly reasonable to say "AI is worsening the experience of minorities in various ways" and to differ with responses that assume with slight additional care in curating datasets that the problem will be completely solved.