Disparate impact isn't about preventing the use of all indicators that correlate with a protected class. However, companies often use indicators that are of dub

Disparate impact isn't about preventing the use of all indicators that correlate with a protected class. However, companies often use indicators that are of dubious predictive power (things where one can't show a significant correlation with the bad outcome the company wants to avoid, but show a strong correlation with a protected class). This is done in a LOT of machine learning settings - the vast majority of variables used in classification/regression scenarios fall into this category. Most machine learning consists of turning a large number of nearly insignificant correlations into a single big correlation. Consider category A, which has features x[i] located in the ball of radius 100 around the point [0,0,...,0] in N dimensional space. Category B has features x[i] located in the ball of radius 100 around the point [1,1,...,1]. Any individual feature is useless - if it's from category A, it'll be located in [-100,100], whereas if it's from category B it'll be located in [-99,101]. On the other hand, the classifier sum(x) works perfectly for N > 100. That's because in N dimensions, the distance between [0,0,...,0] and [1,1,...,1] is sqrt(N) (proof: use the pythagorean theorem). That's an oversimplified example, but I hope it illustrates the point. It's a bit scary that the FTC wants to start regulating feature engineering. Will they also take a position on Bayesian classifiers vs geometric/vector space methods?