> We are discussing a set of AI principles, not an implementation of those principles.

If the position is that any implementation of those principles is inherently problematic, then that is nonsense. Quite frankly, I do think we're talking about implementation.

De-biasing models is necessary. Anyone who claims otherwise... I just don't think that's a defensible position to take if you've ever worked with large datasets. So if you have a criticism of Mozilla's implementation of its de-biasing, then argue against that implementation. But arguing that there shouldn't be an implementation is ridiculous.

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> So how do we know that the model authors aren't just introducing their own bias, either by over-correcting one set of biases from the input, or under-correcting others?

You heckin don't know.

But the alternative to that is not neutral models, it's still biased models -- because the data itself that you train an LLM on is inherently biased. Any large textual database you get off the Internet is going to have bias in it. Choosing to ignore that fact does not give you neutrality.

You're very worried about the risk that de-biasing can become re-biasing, and on one hand, you're right, that's a very real risk. Censorship and alignment risks are very real concerns in AI. But you don't seem to be worried about the fact that models that are not "de-biased" are subject to the exact same concerns.

Choosing not to de-bias is just saying that you'll have a biased model with all of the same concerns you raise above, except with no effort to correct that bias at all. You're scared of Mozilla pushing people to bias their models in a certain direction, you don't seem to have internalized that the models are already biased in a certain direction. And all of the risks of model bias apply to models that have been uncritically fed raw data.

Again, disagree with specific implementations if you want, but it is extremely reasonable for AI researches to say "Internet data biases against certain groups and we would like to correct for that bias when training models." And there's no "neutral" way to do that -- the way you correct for bias is you identify the areas where bias exists and you push back on those trends in the data.

Again, I'd bring up Reddit here. If you're trying to build an LLM on Reddit data and you want it to be "fair" to Conservatives/Christians, that means identifying that a platform like Reddit has a very clear Liberal/Athiest bias and pushing against that bias in the training data. And if someone comes up to you and says, "well, that's not neutral, you're privileging Christians, what about anti-atheist bias", then I just feel like that person doesn't really understand how correcting for data-skew works.

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> but when an organization like Mozilla openly states in their guiding principles that certain groups are a special priority

It's OK to triage issues and attack them in a specific order. Again, if you have issue with which groups Mozilla chose, then fine -- but you seem to be arguing that when de-biasing a model, it's not OK to try and pick out the most impactful biases and triage them and focus on the most important issues first -- and that's just a really silly thing to argue. Of course any attempt to correct for data bias starts with identifying specific areas where you want to correct.