If we were only concerned with making accurate predictions, then all of that would be fine. But the point of the article is less about the accuracy of our pred

If we were only concerned with making accurate predictions, then all of that would be fine. But the point of the article is less about the accuracy of our predictions, and more about the effects that our priors may have on others. If a female technologist attends a tech conference and gets asked 100 times "are you in HR?", it's very likely that she'll feel unwelcome and discouraged. And her reaction to the questions will have absolutely nothing to do with whether the people doing the asking had a "prior" or a "bias". Creating a welcoming atmosphere for women in technology means that you avoid asking that question despite the fact that the prior may be accurate.