Other goals can be to have equal model accuracy across subgroups (e.g., models has the same precision/auroc/etc for each subgroup, even if the predictions themselves differ across subgroups) or identical distribution of statistical biases across subgroups (e.g., the model makes the same types of mistakes within each subgroup)
Like, let’s look at that for credit decisions. Black folks have a lower income distribution. So it’s like saying the model is ok if it rejects a Black applicant making 80k if it would also reject a White candidate making 100k. That is the insane bullshit you’re effectively supporting by desiring comparable error distributions.
You have to accept that equal outcomes are not just impossible but not desirable. And then you only use fields that people agree are fair to use. If Black people get disproportionately better or worse outcomes it is either because those outcomes make sense given their stats or it’s not ok because if a White dude had the same info, that you agree is fair to use, they would have passed and the underlying reason for the difference is some bullshit other input.
That might mean that the model is a dumb: you must make $100k for this loan (illustrative) which is going to mean a lot of Black folks cannot get it. Well that model is at least “fair”. Accommodations for race should not be coming from an AI model
Job applications are kind of dumb. The common use case is credit decisions. Surely, for example, you’re ok looking at credit history and income when deciding to make a loan, right? Even though it won’t be equitable outcomes across races, it is still “fair”