[timmg]

> sing 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.

[em-bee]

young men pay more for car insurance than young women (today). This is based on statistical models. Should they be outlawed?

EU has outlawed them. their argument is that differentiation is only valid if the difference is the actual cause and not merely statistical correlation.

[timmg]

Ironically, in the US it is ok to charge men more for car insurance, since they cost more in aggregate. It is illegal to charge women more for health insurance even though they cost more in aggregate.

[em-bee]

given the economic realities of income between men and women, i think that makes sense.

[tptacek]

It would obviously be very bad if those decisions were being made based on the statistical weight of the training corpus of a general large language model.