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by catalogia
2438 days ago
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Well it may be the case that they accidentally have a proxy for race already in their data (the "this ethnicity prefers red cars" hypothetical in the article above.) So race may already in practice be factored in despite nobody intending for that to be the case (assuming nobody anticipated that a particular metric is a racial proxy.) That does not necessarily mean it's being unfair to that race though. It could, hypothetically, mean that it's actually being fair to that race, advantaging them in a system/society that would otherwise disadvantage them. |
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The original problem was that racists were not just taking race into account but were disproportionately penalizing certain races. They would literally just refuse to do business with black people. And then once that was made illegal, they would refuse to do business with people from black neighborhoods (redlining), i.e. use location as a proxy for race so they could continue to refuse to do business with black people under that pretext.
Normal Bayesian statistics doesn't do that because it's missing the actually racist piece of it, which is giving disproportionate weight to race (or something that correlates with race) so that you refuse disproportionately many people of a particular race for no legitimate reason.
The unfairness never came from taking into account some factor that correlates with race, or even race itself to the extent that it actually correlates with outcomes. It came from using a factor to deny service even though it didn't correlate with outcomes, or if it did then still not proportionately to the huge negative weight assigned to it. It came from giving race, or a proxy for race, disproportionate weight. Giving it proportionate weight isn't unfair, it's the only thing that is fair.