I agree with you when you say fairness is fuzzy. Though this implies that there are no set of rules that define it, that also means there is no way to train on

I agree with you when you say fairness is fuzzy. Though this implies that there are no set of rules that define it, that also means there is no way to train on it. By choosing the right features and utilizing it in the right way I believe you can avoid putting people in bad situations for bad reasons. I'm saying, if you train an algorithm to guess if someone is involved with crime, it's going to be incredibly stereotypical with it's answers. Same as if you rely completely on statistics. I'm not actually saying hardcore all your rules. I was making an example that if you really know exactly what fairness is, then program it. But we both don't know there are no definite rules. So why hardcode at all? I think it is never the ML that is "unfair", it's the one who made it who is responsible. If you're finding yourself running into issues in your ML with unfairness, I think you're just using it wrong. EDIT: Rewording last sentence