But how do you do that? Race is baked into a lot of the attributes your classifier is going to find informative. Is someone a good credit risk? To decide, you l

But how do you do that? Race is baked into a lot of the attributes your classifier is going to find informative. Is someone a good credit risk? To decide, you look at features like past payment history, available balance, zip code, etc. Past payment history: if you're black, prior discriminatory behaviors may have limited your ability to open credit accounts, and thus you have less history to go on. Available balance has the same reasoning. Zip code correlates with race. I'd hope very few people are including an "is_black" feature in their classifiers. If you eliminate anything that is informative towards race though, you're likely going to have a classifier that doesn't work very well. The problem is that we have datasets that have arisen from a history that included significant racism, both overt and latent. There is no way to separate those effects from the data. You either get an "optimal" classifier that is racially biased in ways we don't want, or you get one that intentionally gives up some perceived performance in favor of fairness.