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I'm not talking about ignoring the Twitter thread itself, but rather ignoring the issue it discusses. What I was getting at when I used the word "privilege" is that LeCun is not a member of a group which tends to experience negative effects from these kinds of technological tools -- quite the opposite. If you're a member of one of those marginalized groups, as Gebru is, those issues might become a little more interesting to you. For instance, these kinds of issues, https://www.propublica.org/article/machine-bias-risk-assessm..., https://themarkup.org/locked-out/2020/05/28/access-denied-fa..., https://www.ruhabenjamin.com/race-after-technology

I also work in computer vision (for biological sciences). I use deep convnets every day, and certainly they are increasingly central in biology (https://arxiv.org/abs/1611.00421), proteomics (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3837439/, https://deepmind.com/blog/article/AlphaFold-Using-AI-for-sci...), astronomy (https://arxiv.org/abs/1902.05965), chemistry (https://www.nature.com/articles/s42254-019-0086-7, https://pubs.rsc.org/en/content/articlelanding/2019/sc/c8sc0...), many other sciences. I think it's great, they're enabling progress in lots of really tough scientific problems. And for what it's worth, people are still doing horrible things to mice, salamanders, zebrafish, haha, being a model organism is not a good gig, but of course it's still a huge part of neuroscience...

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.

Actually, that's Gebru's point. Gebru was pointing out frustration that LeCun was focusing on this narrow question of statistical bias, rather than the broader social issues you're getting at.

To be fair, LeCun started a discussion around bias in ML -- Gebru's reply is on topic, since statistical bias in ML is a narrow way of understanding the social impact of an algorithm. Gebru is one of the foremost scholars on that issue.

Adding some more sources to show that these scholars are not raising issues without concrete solutions -- Gebru and others have proposed many solutions and more concrete problems than this easily solved question of statistical bias. They are mostly not focused on the issue of statistical bias, which is more or less beside the point to everyone, as LeCun and Gebru both pointed out.

Podcast:

- https://www.wnycstudios.org/podcasts/science-friday/segments...

Articles:

- 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

Book:

- https://www.ruhabenjamin.com/race-after-technology

He has the privilege to ignore that discussion. That doesn't mean everyone else does, or that other people can't be frustrated with that choice.

That's not the problem. To get a sense of it, let me punt to people who know more than me.

- 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 wanted to write to address your point about the technical assertion, without getting into your request that Gebru empathize with one of the most prominent figures in science.

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.