I didn't miss that. As I said: Now there are statistical issues one might run into - e.g., early overfitting of what is essentially a bandit algorithm, and unac

I didn't miss that. As I said: Now there are statistical issues one might run into - e.g., early overfitting of what is essentially a bandit algorithm, and unaccounted for feedback between training data and system outputs. There is nothing fundamental about machine learning that says seed data like this will give biased outputs - many algorithms do have this problem (it's a difficult one to deal with), but it's not fundamental. I certainly didn't get the impression from the article that it was advocating for algorithms which are less sensitive to these errors. Among other things, that's far less of a conversation that "we have to talk about", but far more of a conversation that some stats geeks have to talk about. These are also far less of an "ethical" problem (as the article asserts) and far more of a technical one. But maybe I misread.