> Nobody says they are not bugs. You are creating an imaginary problem here. You really think, say, researchers at Amazon said "let's make it so that women are ranked down by the algorithm"? Likewise I don't think anybody says "the algorithm should rank black people worse for crime".

They did though, at least until Gebru and those like her came along and forced the issue.

It's really sad to see people say that this was never a concern as though bias and ethics were taken seriously by the field as a whole more than, say, 5 years ago. They weren't. Idk if you're new to the field or weren't paying attention, but it just wasn't a thing. Like most of the foundational papers in terms of racial misclassification and such are from 2017 and 2018.[2] It's more recent than...GANs or AlphaZero. Not to mention that there's attempts to publish garbage like this[1] every year!

> You really think, say, researchers at Amazon said "let's make it so that women are ranked down by the algorithm"? Likewise I don't think anybody says "the algorithm should rank black people worse for crime".

No, I already said this. Someone failing to notice a bug isn't malice. But there issue is that no one even considered that these kinds of things were bugs so they didn't get noticed or researched.

> The whole field is about treating bias - a machine learning algorithm is all about training some bias.

Yes, but thinking about race as a particular category where we should avoid unintended bias (and indeed prefer generalization across categories) was a novel idea when proposed by those ethicists!

> But it wouldn't be the fault of the algorithm. What is insanity, but basically what the "AI ethics" people demand, is to tweak the algorithms to make them pretend the prevalence of criminality is not higher in certain populations.

But...you're making the algorithm. If your goal is to build a model that tries to detect "racial criminality", I'm going to suggest that you probably are doing something racist, because there isn't really a useful, non-racist, reason to train a model that incorrectly classifies people as criminal based on their skin color.

On the other hand, if you're having to do additional interpretation of the model output, why aren't you integrating that additional interpretation into the model? And if you can't, then is the model even adding any value? Probably not.

And that's not even ignoring questions like what "prevalence of criminality". I think you mean "are arrested more often". We often think that that correlates with criminality, and for some crimes it may, but for e.g. drug crimes we know that it doesn't. The point is, if you don't have at least thoughtful answers to all of those questions and more, you have no business trying to do "criminality" prediction, because your algorithm is not doing whatever you think its doing.

[1]: https://www.bbc.com/news/technology-53165286

[2]: Seriously, Gender Shades is 2018, Debiasing word embeddings is 2016 which I think is the earliest you could argue people were taking this stuff seriously, and it cites "Unequal Representation and Gender Stereotypes in Image Search Results for Occupations" from 2015, which is kind of it.