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Culture War Roundup for the week of September 19, 2022

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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.

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).

An alternative explanation is that doublethink required to simultaneously believe in the party line and in the reality required to do your job doesn't actually work very well and tends to devolve into believing in the party line only. Imagine that you're a bright young guy working on a Google's image classifier. To generate the thought that the classifier might confuse black people for apes so you must specifically check that it doesn't, you must believe that black people tend to have certain ape-like facial features. That's a very dangerous thing to believe, your woke peers would be very unamused if you just blurt it out or inexpertly wink-wink nudge-nudge your way to suggesting that you need to check for that etc. If you have a lot of wrongfact beliefs you have to watch your every word to avoid committing a social suicide. Accidentally releasing a classifier that does in fact mistake black people for apes on the other hand is relatively safe: it's not your personal fault and who could have thought and it's probably bias in the training data anyway. So in a highly ideologized environment people just naturally fail at their jobs instead of trying to maintain a bag of forbidden beliefs.

This example doesn't work - black people are dark, apes have dark fur, image classifiers often pick up on easy-to-detect features like color.

More generally I'd question how important the party line/reality conflict is - many genuine smart people believe in wokeness and will probably continue to indefinitely. E.g. OpenAI is clearly woke yet manages to put out a great product.