Comment by djohnston
No... the pre-determined bias in this story is obviously that all subgroups of people behave identically w.r.t. welfare applications, which the data itself did not support and a momentary consideration of socioeconomics would debunk. When they tried to cludge the weights to fit their predetermined bias, the model did so poorly on a pilot run that the city shut it down.
Being flagged as potential fraud based on eg. ethnicity is what you want to eliminate, so you have to start with the assumption of an even distristribution.
From the article:
> Deciding which definition of fairness to optimize for is a question of values and context.
This optimization is the human feedback required to not have the model stagnate in a local optimum.