Some of the major issues with this come in with omitted variables. For example, suppose for a second that low wage earners cheat the system more. Suppose that r

Some of the major issues with this come in with omitted variables. For example, suppose for a second that low wage earners cheat the system more. Suppose that race is correlated with wage for historical reasons. Suppose that at each given wage level race has nothing to do with cheating the system. If you don't include wage data in your system, you'll just see race and fraud connected. Now you've created a tragic feedback loop you didn't need to create. What if somehow we as a society magically get equality overnight and there's no more connection between race and wage? But you use historical data where they were connected, still excluding wage data, and so even now your model connects race and fraud. In this hypothetical example, I'd argue it would be inethical to include race without wage. Hell, wage is only going to correlate with fraud via yet other variables. Maybe an ethical system needs to go out and gather those measurements too. (side note, in an ML system with regularization, even if you do include wage in your data, the regularization might pin some fraud on race anyways.) The solution is to acknowledge that there are always going to be omitted variables and either a) be extremely careful and rigorous in your data gathering, design, and roll out or b) don't try to automate this thing, leaving it the slow expensive way where people can gather facts as needed, case by case.