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Comment by IfOnlyYouKnew | Hacker News Reader

No, "I'm so tired" means that it's an argument they have replied to before. She has written dozens of research papers, some of which probably contained arguments other than repetition of "you're a racist and a bad person".

You of course know about this, or would now about this considering she is called an "AI researcher at Google" in the title of this tread. So the only lazy and un-constructive criticism is to assume she has not made any arguments beyond your one-line caricature of what you believe her to be.

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Then what is Gebru's solution to the FlickFaceHQ database having too many pictures of white people?

Expanding the dataset and/or using a subset of it?

Gebru would disagree:

> “I think that now a lot of people have understood that we need to have more diverse datasets, but unfortunately I felt like that’s kind of where the understanding has stopped. It’s like ‘let’s diversify our datasets. And that’s kind of ethics and fairness, right?’ But you can’t ignore social and structural problems.“

This seems to be saying that Yann LeCun should actually be addressing social and structural problems, rather that just applying neural networks to data sets.

https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter...

IMHO she is saying it’s reasonable to believe that social and systemic issues have an impact on creating ML models (as the teams designing, building, and deploying said models are members of society) in ways in addition to the data sets.

I don’t know what the right answer is but plenty of institutions come up with ways they attempt to deal with systemic biases. For example, blind auditions for positions in orchestras (you only hear the person auditioning, you can’t see them). Used to not be a thing. Now it is.

Point being that when people are motivated they are able to come up with stuff.

Sure, but Yann LeCun is a neural network expert. Should it be his job to tackle social/systemic/institutional problems? What if he doesn't want to?

I think the point was reciting the (true!) 'biased datasets create biased models' argument at best avoids, and at worst actively undermines those bigger questions.

Someone in another thread on this had IMO a great comment[1] summarizing the sentiment:

> If you are going to say "Well, garbage-in, garbage-out!", why do you keep putting the racist garbage in?

[1]: https://news.ycombinator.com/item?id=25286446

It's really a stretch to call the Flickr-Faces-HQ Dataset "racist garbage."

It's a collection of images uploaded by random flickr users with permissive licenses.

https://github.com/NVlabs/ffhq-dataset

Yes it should if the networks he builds have an actual impact on real people because of their social/systemic/institutional status. If he doesn't want to do it, he shouldn't be working on networks that have this kind of impact.

What if you're a nuclear engineer that just wanna make the hot bit go hotter, and don't want to care about the nasty habit that radiation has of killing people?

Google “Gebru arxiv”.

If you’re confused as to whom Lecun is, google also “lecun arxiv”.

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