I don't know about this. I'm on board with not using race as a covariate -- people are born with their skin color and to penalize someone's loan application over something they're born with and have no control over is unfair.

But zip code? I understand there is empirical data showing how race is correlated with zip code, but you still choose your zipcode at the end of the day. If you have data saying that applicants from "Zipcode X" are less likely to repay loans, why is one's choice of zipcode out-of-bounds but their choice to have a hard credit check several months ago not? What if a poor credit history is correlated with race (fwiw I don't know if it is or not)? Is it then discriminatory to use credit history as a covariate?

The truth is that machine learning to predict people's behavior is inherently discriminatory and involves generalizations by definition. Once you start disallowing training on covariates that exist due to voluntary behavior, it's not really clear whether you should be allowed to use machine learning to predict individual behavior at all (I'd probably listen to that argument actually).

There's clearly a tradeoff to be made in terms of how much discrimination is allowable in exchange for a given unit of predictive performance, but that sounds incredibly difficult to regulate for every business's ML problem. I don't know what the answer is, but I think it's hasty to argue that anything that's correlated with race is out-of-bounds.