The problem here is, we don't even know what "fair" means. What do we mean by fair? Say we want to base fair on group outcomes, in which ways should we divide people into groups in order to perform some sort of balancing? Is hair color or texture a relevant way of subdividing people for instance? Are we being "fair" to red haired people? What if red haired people for some reason seem to be less likely to pay back loans? Will we need to adjust our credit rating model to bump the credit scores of red haired people slightly? Will we need to agonize over other features fed to our model to ensure that they can't be used as a proxy for red hair?
Maybe???? But it seems very dishonest.
I think, if anything, the models are probably too true for our tastes as social creatures who evolved to avoid social conflict (being 100% honest with your unruly neighbors can lead to a loss of harmony that is much more damaging than just putting up with some disturbances). Our idea of "unbiasing" the models is more likely actually making them biased in a way that pleases our monkey brains.