I do understand the point you're making -- my concern centers around deeply flawed models being used despite their shortcomings because their inherently progres

I do understand the point you're making -- my concern centers around deeply flawed models being used despite their shortcomings because their inherently progressive slant is sacrosanct. They would instead be held up and fingers pointed elsewhere, either due to lack of data or incorrect usage of the resulting advice derived from existing bodies of data (or something more sinister, eg: sabotage by opposition parties). What I'm trying to say is correcting discrimination under the guise that it's unfair to certain sections of society is chasing your own tail out of boredom -- what's the point? To improve society, or improve usefulness of machine learning-derived data to make radical (but necessary) decisions? Do we maintain our objectivity and move away from "improving society" when it's found to be counter-productive, or do we keep going forward with improving society and hope things get better? Trying to "break out of the status quo" to produce more a accurate representation of the world sounds great, I'm totally all for removing human biases, but my concern is that it'll be handled badly to mistakenly attempt to benefit certain classes of society (eg: repeat of subprime mortgage crises from 07-09 but much more difficult to diagnose).