And I work with credit risk models and can tell you there is a negligible chance that gender is causing a 20x credit limit increase. No protected variable will have so much influence, and Apple/GS are not even legally allowed to directly reference such variables.
It may be the most logical conclusion for the general public, but all they see is the output of a black box. DHH jumped to conclusions that it had to be something about gender, with n=1.
Who says there is a problem that needs fixing? Demanding that women and men always receive equal limits is not about fairness, it is a very radical notion that makes discriminative credit risk scoring almost impossible.
I am not saying that Apple/GS did not mess up here, and could have been more transparent, but, in their defense: Oftentimes you can't be more transparent (like when Google closes an account for posting something illegal, they have to keep mum, while the user loudly complains online that they got banned for doing nothing), an help desk person is never going to be able to give an explanation for a model decision (this is usually a good thing), and DHH seemed to be barking up the wrong tree all together (what if the different limits were the result of a, completely legal, influencer program or randomized trial?).
It really does matter how it got that way. If 20% of people of color vs. white people have a low income (a non-protected variable), I am perfectly allowed to use that variable to deny them a loan more often, even if the result is that 20% of people of color don't receive a loan. That's not a racist algorithm, however it would be racist if I start ignoring income of certain people as to achieve racial demographic parity in loans.
Similarly, as this is an ongoing investigation now, and they probably anticipated that, Google was probably legally very limited in what they could disclose. I bet some people with all the knowledge of the data - and colleague snooping are shaking their heads at some of the public perception of this case as union-busting.
The opposite of accepting a coincidence once in a while is viewing everything through the lens of intentional discrimination.