Yes, it would be extremely bad if the statistical weight of the total corpus of training data caused a system using an LLM to make decisions about extending credit to offer worse terms (say) to women.
In general, or if it isn't the correct answer?
Like: young men pay more for car insurance than young women (today). This is based on statistical models. Should they be outlawed? I think that is a very interesting question (but they aren't, today).
If the LLM was in charge, would it be wrong for it to charge young men more? Should we train that "bias" out? Or should we only train out biases that are wrong? And would that be different than how we train them today?
I don't know the answer. But I think it is less obvious than some people seem to think.
EU has outlawed them. their argument is that differentiation is only valid if the difference is the actual cause and not merely statistical correlation.
At least LLMs offer a way to be tuned against that. Not that their creators would be interested in that, since the LLM's bias is exactly the mainstream opinion that they like very much.
I’m trying to understand what people think we should correct for.