Well said. I especially appreciate the distinction you're making between balancing liberty with liberty-destroying ideology. This is the kind of difficult nuance that I rarely see in these discussions. One one side there's the free speech absolutists, whose arguments tend to ignore the fact that unmoderated propaganda, and hate speech tends to be more addictive, and spread ignorance faster then fact-checking can fix it. The consequences of this sort of callous attitude are literally genocide[1]. On the other hand, there's the 'cancel-culture mobs' (for lack of a better term) which are now censoring regular speech that disagrees, or appears to disagrees, or isn't sufficiently subservient to their opinions. Just yesterday I was sadly reading this depressing thread where Yann LeCun was run off twitter[2] for explaining how bias (in the social science sense) can be traced back to various steps in the ML pipeline (in this case, mainly a feature of the dataset itself, but also the choice of errors, bias vs variance, etc). The inability to admit nuance is the only thing I can think both these groups share, and maybe what needs to be emphasized more. [1] nytimes.com/2018/11/06/technology/myanm…... [2] twitter.com/ylecun/status/1274782757907…

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]: twitter.com/ylecun/status/1203211859366… [1]: twitter.com/ylecun/status/1274782757907… [2]: twitter.com/ylecun/status/1275162732166… [3]: twitter.com/ylecun/status/1275167319157… [4]: twitter.com/hardmaru/status/12752143815…. 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.

This person forced Yann LeCun (one of the top 3 AI/ML researchers today, head of Facebook AI, and an ML researcher since the 80s) to give up on Twitter after she took exception to this tweet of his: twitter.com/ylecun/status/1274782757907… She lives her life attacking people who she disagrees with, and brings in race/gender into any conflict where she's involved. I am surprised Google had hired her, because it was only a matter of time they'd become collateral damage in this roving tornado of hate and aggression. From what I have seen today, she gave them an ultimatum: do this, this and that or else I will leave. They decided to not do those things she demanded, and then proceeded to fire her before she could do any more internal damage.