Why not pick all those other hills to die on, then?

If you want to talk about statistical methods being a horrible idea for policing, I'm with you. Probably most machine learning experts would be, too, just like most software engineers don't trust electronic voting. Instead, people chose "dataset bias affects machine learning models", the most basic Machine Learning 101 fact ever, to try and debate against.

It's like the woke people seek out the weakest possible ground because it generates in-group opposition that they can fight with and out-woke. Stronger ground could have national consensus, way beyond bay area consensus.

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.

Your comment, among the sea of others, got through and made some sense to me. My follow-up questions are:

Does that negate the truthfulness of LeCun's response? What would Gebru rather he do or say?

I feel like I can't tell what the desired form of action is from the article, and I'm unsure what good it does for removing bias from ML to attack prominent figures (on twitter let alone anywhere public).

> Instead, people chose "dataset bias affects machine learning models", the most basic Machine Learning 101 fact ever, to try and debate against.

No one disagrees with that statement. The point that the ethicists are making is that that isn't the whole story, and that focusing only on the lowest hanging fruit (dataset bias) is ultimately harmful since it shifts focus away from the other, deeper problems.

I'm not clear about how Yann does not take it seriously? He even admitted respect for Gebru's work. He's just making a technical claim that this is due to data bias. Whether that's true, who knows, but I really don't see how he is being callous in all of this.

I'm not sure I understand what the issue is at all.

To agree with your point, LeCun did many things which indicate he takes this seriously:

1. Responded with a detailed summary of his views.

2. Said he was open to changing his mind in the face of convincing arguments; indicated a desire to learn.

3. Asked people to stop attacking his opponent, in response to unfair attacks on her.

4. Only a week ago, sent multiple tweets out about the problems with a paper which demonstrated racial bias. https://medium.com/@CoalitionForCriticalTechnology/abolish-t...

There's something in between not taking someone seriously and agreeing with their argument.

The problem is, the "Lions of AI" do take them seriously - they just aren't beating their drums about it and making it the top thing they shout about.

All of the same concerns are discussed in their works about representational data and biased algorithms etc...