If one of these models is on average better, then they would gain an advantage by using it. The problem is for the "not normal enough" folks, it may be _harder_ to remedy an invalid classification, particularly if there are no fallbacks or work arounds. I was cued into this once by an ML book that gave an example of a fraud detection company using an actually worse algorithm, because when it gave false positives it was easier to understand and hence easier to manually override. But if it is less profitable to operate this way, and there is no regulation around it, people getting falsely classified may be out of luck. That's where the discussion around regulation needs to happen, I think.