Not really, Yann's original claim (which he sort of kind of partially walked back) was that data is the only source of bias [0][1]. He walked that back somewhat

Not really, Yann's original claim (which he sort of kind of partially walked back) was that data is the only source of bias [0][1]. He walked that back somewhat to claim that he was being very particular in this case[2], which is perhaps true, but still harmful. The right thing to do when you make a mistake is apologize. Not double down and badly re-explain what other experts have been telling you back at them. So then Yann notes that generic models don't have bias[3]. This is, probably, true. I'd be surprised if on the whole, "CNNs" encoded racial bias. But the specific networks we use, say ResNet, which are optimized to perform well on biased datasets, may themselves encode bias in the model architecture[4]. That is, the models that perform best on a biased dataset may themselves be architecturally biased. In fact, we'd sort of expect it. And that all ignores one of the major issues which Yann entirely skips, but which Timnit covers in some of her work: training on data, even "representative data" encodes the biases that are present in the world today. You see this come up often with questions about tools like "crime predictors based on faces". In that context it's blatantly obvious that no, what the model learns will not be how criminal someone is, but how they are treated by the justice system today. Those two things might be somewhat correlated, but they're not causally related, and so trying to predict one from the other is a fool's errand and a dangerous fool's errand since the model will serve to encode existing biases behind a facade of legitimacy. Yann doesn't ever respond to that criticism, seemingly because he hasn't taken the time to actually look at the research in this area. So insofar as data is the root of the problem, yes. Insofar as the solution is to just use more representative data in the same systems, no. That doesn't fix things. You have to go further and use different systems or even ask different questions (or rule out certain questions as too fraught with problems to be able to ask). [0]: https://twitter.com/ylecun/status/1203211859366576128 https://twitter.com/ylecun/status/1203211859366576128 [1]: https://twitter.com/ylecun/status/1274782757907030016 https://twitter.com/ylecun/status/1274782757907030016 [2]: https://twitter.com/ylecun/status/1275162732166361088 https://twitter.com/ylecun/status/1275162732166361088 [3]: https://twitter.com/ylecun/status/1275167319157870592 https://twitter.com/ylecun/status/1275167319157870592 [4]: https://twitter.com/hardmaru/status/1275214381509300224 https://twitter.com/hardmaru/status/1275214381509300224. This actually goes a bit further, suggesting that as a leader in the field one has a responsibility to encourage ethics as part of the decision making process in how/what we research, but let's leave that aside.