> I don't know how to be more clear: nowhere in this thread have I argued for using raw models without any de-biasing.

Okay, great. But then we are arguing about an implementation, aren't we? We agree that de-biasing is necessary. It's just that you seem to think that calling out specific biases is the wrong way to approach that.

But I'm curious how you're supposed to de-bias data without drawing any attention to or acknowledging the groups that the data is biased against?

> The idea that "Internet data biases against certain groups" seems itself a biased statement.

If a data set is biased, it's biased in a direction. By definition, that's what bias is.

> I am pretty sure that Internet data is biased against all groups. I bet you can get a raw LLM to say offensive things about any group if you give it the right prompt.

That's not what bias is. Bias is not "can you get it to say something offensive" -- bias is a systematic predisposition in a direction. Bias is not stuff like, "it made a dirty joke", bias is stuff like, "this hiring model is more likely to hire you if your name sounds white."

And the goal here (believe it or not when I say this) is not to make models that never make offensive jokes. The goal is to avoid making models that reproduce and reinforce systemic issues in society. It's entirely appropriate when looking at the problem of "what social inequities are being reproduced by a model" to identify specific social inequities and inequalities that exist in society.

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Part of the reason why I've been assuming that you're against de-biasing in general is that you keep saying stuff like "the idea that the Internet biases against certain groups is itself a biased statement."

That is nonsensical, it seems like you're suggesting that companies should be correcting against bias without determining what the direction of that bias is or who it affects. How exactly should they do that? What does it mean to correct a systemic skew without identifying what the skew is?

Again, look at the Reddit example. Yes, you can find offensive stuff on Reddit for everyone: atheists, vegans, Conservatives, Liberals, whoever. But de-biasing is not about whether or not something is offensive, it's about identifying and correcting a predisposition or tendency towards certain attitudes and thoughts. And that necessarily requires calling out what those predispositions are -- so you're going to end up with statements like "Reddit is biased against Conservative/Libertarian philosophies overall, on average."

And if somebody jumps out and says, "Reddit is biased against everyone, it's itself biased for you to call out that specific bias" -- then there's no response to that other than that's just not how any of this works. If you go into a conversation about data bias saying that it's inappropriate for us to identify specific biases, then you are effectively arguing against reducing bias, regardless of what else you say.

And even more to the point, even if that wasn't true -- fixing any systemic problem starts with triaging and identifying specific areas you want to target first. It is so weird to hear someone say, "I've never claimed we shouldn't fix the problem, but pointing out specific groups that the problem applies to is wrong."

No, it's identifying problem areas that are high impact: a normal, good thing for researchers to do. To the extent that it's "biased", it's the kind of bias that we want in pretty much every field and on every subject -- we want researchers and debuggers to bias themselves towards focusing on high-impact areas first when they're fixing problems. And we want them to call out specific areas that they think deserve attention. That's what triaging is.