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I wrote a post about de-biasing efforts in machine learning, which got a bit long, so I decided to turn it into an article instead. It's about how corporate anti-bias solutions are mostly only designed to cover their asses, and does nothing to solve the larger (actually important) issue.
(As an aside: does it still count as a "bare link" if I point to my own content, just hosted elsewhere?)
You are right, they are not really trying that hard. Anybody smart enough to build bleeding-edge AI systems is smart enough to understand why if you try to predict the likelihood of a criminal repeating a crime, it will always say that black people are more likely to repeat (it's because black people are more likely to repeat). The problem is fairly hopeless, because AI's accurately that black people are more likely to commit crimes, women are for the most part uninterested in studying machine learning, and other things that true but verboten.
So their manager asks them to do something about bias, and they apply the laziest possible hack. I think this disinterest is more prominent in top-tier researchers. Low-end researchers who will never accomplish anything useful are happy to feast on the de-biasing funding teat.
There are some other niche cases, like facial recognition software not recognizing blacks. But this requires no special debiasing effort, it is simply a weakness in the system that can be addressed the same as any other weakness.
Obviously, the only possible "de-biasing" technique that can work is explicitly biasing systems against white men. If two criteria are mutually conflicting such that one group or the other must be "discriminated" against according to one criteria or the other, choose the criteria that discriminates against white men (in that order: first discriminate against whites, then discriminate against men). It is very simple.
My article really only covers generative models, like the recent Stable Diffusion. Controversial models like classifiers that try to evaluate how likely somebody is to commit a crime has entirely different considerations. Maybe I should have made that more clear.
Also I disagree that a "de-biased" crime model would discriminate against white men! Men commit a highly disproportionate amount of crime compared to women; any sort of adjustment you make has to adjust for that, adding a whole bunch of likelihood on women especially, probably more than the racial difference even.
You are missing the point. In de-biasing, blacks will receive an adjustment that favors them, whites will not. Women may receive some adjustment that favors them, men will not. If some model rates men negatively, this is because of the deficiencies of men. There is no need to debias the model: men are simply worse, as the model captures. If the same model rates blacks negatively, this is a flaw of the model and it must be de-biased.
This double standard is very obviously the consequence of radical anti-racist ideology. Bias is privilege + power. You can't be biased against whites or men. It is by definition impossible.
I don't think I am. I agree that a naïvely de-biased crime model will favour blacks over whites compared to a model that just went for simple accuracy and nothing else, but men will also necessarily similarly have to be favoured. If not, people are immediately going to notice the model convicting men and freeing women even when the facts are identical. There is absolutely no way people are going to accept that; radical anti-racist ideology isn't that powerful. Adding even more weight in favour of women would just be silly.
(What is slightly more realistic is if the model somehow gets access to a variable that correlates with gender but also crime itself, like your level of testosterone. With that, apologists may explain that the model convicted a man for e.g. murder based on his hormone levels which made it likely that he'd been aggressive; when in reality the model considered that to be rather unimportant compared to it being able to figure out that it's analysing a male.)
Yes they will, and yes it is? We are already passed the point of naked favoritism (look at SAT scores required to get to top universities, segmented by group). There are some complaints, but most (of those who count) are happy to accept it.
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