It is entirely obvious that biased results are the results of real-world biases, at least in most cases. That explains them, but it doesn't excuse very much. Re

It is entirely obvious that biased results are the results of real-world biases, at least in most cases. That explains them, but it doesn't excuse very much. Reproducing, for example, incorrect hiring or credit decisions is wrong independent of the causality, and harms both those individuals and society (yes, banks lose money when they turn down perfectly good customers for irrelevant reasons) One misunderstanding in this debate is that AI researchers get very defensive and retreat to, basically, "I'm not racist". What they miss was that (in most cases) they were never accused of anything like that. Everybody understands how these biases arise from the data, and are sometimes amplified by it. What they can sometimes be accused of is not checking for the most obvious flaws in their models before publishing them. At this point, the problem is well-known and there's simply no excuse for not checking. And if that means your model cannot be released because you don't have the data, that's too bad, but your work just isn't up to purpose. Go find better data. We don't let people sell rotten steaks just because the stores are out of the fresh variety, and there's no reason to treat AI differently.