This isn't allowed anymore, for better or worse. If you don't talk about things with a social justice lens on Twitter, you're liable to get called out as ignorant. It's the #1 subject on the site right now, and no place for discussing anything remotely adjacent to it without invoking it.
In fairness, if you're gonna post a statement, you better go full bore with it with all the keywords in it. People were circulating rubrics on Twitter to grade social justice statements and brigading those who didn't hit all the notes.
If I read it right...a person posted a photo that turned pixelated Obama, a half white person, into a fully white person.
A person, who I assume is some ML expert, explained that it's because the training set used uses too many white faces.
A person replied very negatively to this answer, saying she's tired of it, that he doesn't listen, and talking about injustices in the world.
I don't think either of them are wrong, I just don't understand the leap. Turning pixelated Obama into some random white guy isn't a social injustice. I kinda felt like both weren't even participating in the same argument.
That doesn't mean we should condone her decision to start yelling about it. Shouting matches are a bad thing, even when they take place on Twitter instead of a hallway - most people don't want to work in fields where disputes are resolved through shaming and caps lock.
- https://www.propublica.org/article/machine-bias-risk-assessm...
- https://themarkup.org/locked-out/2020/05/28/access-denied-fa...
- https://ainowinstitute.org/AI_Now_2019_Report.pdf
I can recommend more if you get through those.
I actually sell computer vision software - specifically designed to help reduce subjective evaluation of aesthetic material performance. Most computer vision is real prosaic. It's not scifi. You read a bunch of papers about horrible things done to marmot eyeballs in the 60s, build a discrete model because neural networks are notoriously hard to support in real-world applications where you have to explain yourself to a chemist, and move on. Her work is justifiably marginal.
Personally, I don't think that an interest in the social impacts of one's work as a researcher should be considered hype -- shouldn't one be interested in their impact on the world? For computer vision in particular, these systems are widely deployed. Also, they are just part of a larger apparatus of automatic decision systems that everyone now interacts with every single day, from getting your insurance rates to getting a loan to... Those have now become prosaic too -- certainly not scifi.
LeCun focused on the narrow technical question of statistical bias. Gebru and many many others have raised the point that an algorithm which works equally well for all skin tones will still have a societal impact, and they want to question that impact. How does a given new technology shift power? In whose favor, and at whose expense? That's the question which is arguably more important than some accuracy on a benchmark -- which as LeCun points out is easily solved with a more representative dataset.
Have you engaged with any of the critical study of AI, from Gebru or others, like the AI Now Institute at NYU or Ruha Benjamin at Princeton, or one of the lots of other scholars even in that one Twitter thread? The results of their work have included the recent IBM and Amazon moratoria on the sale of computer vision technology, and likely others to come.
The fact that LeCun refused to engage with a valid and deep criticism of a paper (which was not even his), to me at least, indicates a lack of willingness to engage critically with the social impacts of computer vision. Gebru's tweet pointed out that lack of willingness and her frustration with it, and also the fact that LeCun has had ample time to hear of it (it is in the news a lot, especially after these Amazon and IBM announcements, but groups like AI Now have been describing this for years.)
How about some empathy for Gebru? I would argue that she actually has more on the line than the single most celebrated individual in computer vision in this situation.