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I absolutely agree. Probabilistic statements are fine and useful. Assumptions X equals Y in speech probably not.
How to exactly address this is not quite clear, that's why I thought the article was quite interesting.
(and obviously fixing the labelling of professor = man in text generation is actually much less useful for gender equality say than dealing with actual issues that lead to the P(X|category1)!=P(X|category2))
I don't think it is very high in the list of priorities, but I have no problem in making sure that those biases are not present in the ChatGPT output. This is not suppression of facts. The article is not talking about suppressing phrasing that right now you are more likely be a man if you have a Nobel prize, but it is talking about not assuming that you must be a man if you have a Nobel prize.