I'm not.
Many other rhetorical moves have a lot of politics to muddle through / implicit assumptions that can be disavowed in bad faith, etc. I don't like to engage in that kind of discussion at all.
But the "I'm so tired" bit is both condescending (and it takes... something... to condescend to Yan LeCunn) and evasive. The end result is authoritarian -- look man, this is decided and settled, you haven't kept up.
Calling this out is interesting because it evidences on the contrary that the discussion isn't settled at all, and that she's unable or unwilling to frame it in a convivial manner that's conducive to progress.
I sit out of conversations I'm tired about.
But isn’t what you described so vague and subjective, that you can apply it to pretty much anything?
IMHO it’s more honest to say “I sit out of conversations I don’t like”, which is an entirely fair and understandable decision to make :).
> You can disagree with that reasoning (although you'd be wrong). What you can't do, as a leader in AI, is to pretend to have never heard of it, or that it is so obviously wrong that it warrants no reply whatsoever.
There's a third option. You can just decide to not engage with the condescending prick at all.
We have one of those at work. His area of responsibility is a very cross functional one, and while it is important, he have a great deal of difficulty getting people to participate. Because participation usually means getting told you're an idiot. Loudly, forcefully, and with more words.
That seems to be the case here, only substituting "idiot" out for "racist".
Am I saying something controversial or micro-aggressive here? So be it.
I have the distinct impression that what they want is for discussions to become personal. Don't let them get to you.
Well, the disagreement is about publication. Yann LeCun himself said that "The consequences of bias are considerably more dire in a deployed product than in an academic paper."[1]
Why would it be wrong to publish research based on biased data?
I assume she thinks that Yann has enough intellectual capacity to improve situation of biased ML but doesnt execute. Further, my guess is that the reason why he doesnt execute is not transparent to her, hence frustrating.
Dr. Gebru, among others, expects and insists that they do go beyond that and try to get involved in the social consequences of tech. They refuse, ergo conflict.
Yann has no obligation to do so on Twitter, and shouldn't be called out by coworkers publicly regarding that decision, in my opinion.
Yann was talking about a specific model, she was talking about ML & society on a grand scale. Can't we talk about specific things now? Why did he have to apologize for it?
My feeling is that Timnit is using people, she guilt trips, humiliates them online, makes a big fuss and it's all to whip up support in her crusades. She wants to win the public opinion by any means, so she needs famous opponents to pile on, such as Yann and Dean (who have universal admiration in the field). She's always playing the victim. I find this kind of behavior toxic, it degrades dialogue.
Isn’t Gebru an “AI researcher”, after all?
Every practitioner already knows that these models behave according to the data they are trained on. The general public has no use for this explanation because they aren't the one deciding on the training data. So the only practical purpose is to offer some superficial causal mechanism that sounds like an explanation to that public, presented as an unchanging fact of nature, and distract from the equally true fact a biased model is only published or used iff it is trained on biased data and someone decides to go ahead and publish/use it nonetheless.
Yeah GP is modeling exactly the behavior they're railing against. Claiming that something is wrong without any practical insights for how that thing might be better.
In this case, the issue is that "it's the dataset" is attacking a straw man: nobody is accusing ML practitioners to be explicit racists actively conspiring for their models to be racially biased. Everyone understands that it is (often) a result of the underlying dataset.
But, the argument goes, understanding the genesis of such bias isn't enough to excuse it. At least not when those models are put to actual use in, say, law-enforcement contexts. Or credit agencies, or hiring decisions, or really anything.
If your dog happened to bite any red-haired kid it came across, it doesn't matter that you think red-haired kids are just as good as others, or that the dog was once mistreated by a red-haired kid. You're not going to allow the dog to get near any red-haired kids. And if you do, you'll face charges of at least negligence when some red-haired kid gets mauled.
To tie this back to AI, it would mean that as long as your models produce racially biased results, they simply aren't ready for deployment or publication. Go find better data until your work is no longer liable to inflict harm on anybody.
That's the standard for all other industries: if your car is shown to reliably kill pedestrians below a certain height in crash tests, you go back to the drawing board. Adding a sticker "don't operate car in the vicinity of children" isn't enough.
You can disagree with that reasoning (although you'd be wrong). What you can't do, as a leader in AI, is to pretend to have never heard of it, or that it is so obviously wrong that it warrants no reply whatsoever.
> have to call out the "I'm so tired" bit every time I see it.
so... I guess you're tired of it?