Suppose I dismissed cryptanalysis by suggesting anyone involved in breaking cryptography ought to produce better themselves.
Suppose I dismissed a piece of medical research that shows some drug is no better than a placebo, and I demanded that those researchers produce their own drug that works better than the one they presume to criticise?
Critique is valuable. The entire field of science itself is built on absorbing critique, and checks and balances to ensure we reason out of robustness rather than hasty assertions.
In other words: suppose Gebru identifies that some particular ML model is racially biased. That insight, if true, is in itself a valuable contribution to the field. Suppose Gebru further develops an argument that the nature of this bias is (or is not) one in which different choices in training data will not substantially solve. That, too, is itself a valuable contribution to the field, and this kind of work is not at all the same thing as pointless "outrage".
I think the Yann's main point is that this specific model's racial bias was a training data problem; Gebru has not provided any evidence otherwise, as far as I know, apart from repeatedly asserting that better training data won't fix the problem.
It would indeed be a valuable contribution if Gebru could have shown how better training data would not have fixed the problem (e.g. by feeding better training data to the same model and still reproducing the issue).
But as it is, she is asserting that better training data won't help without proof and not recommending any alternatives, while taking a hostile tone of conversation. I would be hard pressed to take her claims as good faith criticism.
Suppose I dismissed cryptanalysis by suggesting anyone involved in breaking cryptography ought to produce better themselves.
Suppose I dismissed a piece of medical research that shows some drug is no better than a placebo, and I demanded that those researchers produce their own drug that works better than the one they presume to criticise?
Critique is valuable. The entire field of science itself is built on absorbing critique, and checks and balances to ensure we reason out of robustness rather than hasty assertions.
In other words: suppose Gebru identifies that some particular ML model is racially biased. That insight, if true, is in itself a valuable contribution to the field. Suppose Gebru further develops an argument that the nature of this bias is (or is not) one in which different choices in training data will not substantially solve. That, too, is itself a valuable contribution to the field, and this kind of work is not at all the same thing as pointless "outrage".