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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.
I dunno, the parent comment by sulla strikes me as basically calling out a similar (though more inflammatory) situation. We have two possible meanings of the term "bias" in common use, and these two meanings are:
Not faithfully representing statistical realities present in the data.
Not faithfully representing the statistical outcomes that we would like to see in the data-- most commonly, which reflect reality except for not showing differences based on race or gender.
These are, of course, mutually exclusive definitions; e.g. as pointed out in your article the president of the United States should always be drawn as male using definition (1) and should half the time be female using definition (2) . Likewise, classifiers determining how likely someone is to commit a crime ALSO have to make a decision between definitions (1) and (2) while facing the complicated issue of how to avoid public controversy over admitting that these are different things.
You suggest a third, equally plausible definition (3): "Not faithfully representing the statistical outcomes present IN REAL LIFE (as opposed to just the data being trained on)."
That actually strikes me as brutally difficult, running into the same basic issues as fact checkers do now-- evaluating what is true or false in real life is really hard and intersects with political agendas in such a way as to make it even harder. And how do you even evaluate if you succeeded? Don't get me wrong, i think it's reasonably likely that some generative models will get fine-tuned on specific datasets curators will have labeled as similar to "real life" along various dimensions. But I would not anticipate that this will end up becoming the norm.
As an aside, I think it makes a lot of sense that fundamentally the problem being solved by companies is "how do we stop journalists from agitating about our platform", not anything more interesting or important, and the "debiasing" solutions put in place reflect this reality.
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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.)
People have already noticed this IRL and people already accept it just fine, no radical anti-racist ideology needed. It's just the reality of the situation, sans any sort of ideology, that this sort of bias is fully and openly accepted.
But to the actual point of the thread, I think you are missing the point. I don't think sulla is describing a crime model that's de-biased "naively," but rather one that's de-biased in the most likely way that it is to be de-biased, which is by explicitly putting the thumb on the scale against disfavored groups such as whites and men. A universe in which real de-biasing efforts implemented by real institutions tend to follow some "naive" implementation rather than a politically convenient one seems like a neat universe to live in, I imagine.
Yes, but I could probably have been more clear: I am not claiming that society will demand AI models that necessarily treat men more fairly than we do today! A model with no anti-bias applied will consider men by by default to be extremely likely offenders, especially for violent crime. It is likely that any model can get a good training score by just looking at the gender and ethnicity, and if it's e.g. an Asian woman just let her off the hook immediately.
This effect will be sufficiently extreme to get noticed, and counteracted, by adding bias in favour of men or against those women – likely not enough to make the model as a whole to favour men more than women, but it will still be adjusted away from reality in a way that favours men! An AI that randomly decides to imprison men 50% of the time and women 10% of the time can still be biased against women if women commit 0.1% of the actual crime.
In sulla's initial reply he stated that the model will be biased in favour of blacks, and biased in favour of women, which are both true but only true if you use two different definitions: "manually adjusted to favour a group" or "returning different results for different groups, all else being equal". I assume people think my reply denied that women will be a favoured group under the second definition; I do not.
That's precisely what I meant with "naïvely", as opposed to other complicated schemes (such as the case with generative AI where you could potentially do tricks like adding "no discrimination" to the prompt or the like). Apologies if that was unclear.
And this is where I think you're missing the point. Perhaps the effect will be sufficiently extreme to get noticed; extreme discrepancies refuse to get noticed all the time depending on political expediency, but this could be one of those that does get noticed. It doesn't follow that there will be any desire to counteract this by people who tend to push for de-biasing such algorithms for the purpose of demographic-based justice.
Also, I don't know how to square your above explanation with:
Today, people notice that human-based sentencing systems are "biased" against men in the sense that men and women with equivalent records and crimes get sentenced very differently, with men getting more harsh sentences. People evidently have no issue with this apparent "bias" regardless of whatever lip-service they might pay.
You seem to be claiming that people will notice that AI-driven sentencing systems are "biased" against men in the sense that men and women with equivalent records and crimes get sentenced very differently, with men getting more harsh sentences, and that people noticing this will want to counteract that "bias" by putting their thumb on the scale in favor of men in these AI models. This seems to me to be similar to society demanding that AI models treat men more "fairly" than we do today.
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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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