There has been plenty of research that shows LLMs encode social biases. It seems pretty obvious even before looking at the research that training on the whole internet will end up encoding widely-held social biases and stereotypes.
There's plenty of research into biases in LLMs, and there should be; it's a fundamentally new branch of computer science that could have profound impacts on how we automate and regiment social decisions in the future (like extending credit). The bias concern is well taken in those settings. But it has very little to do with the overwhelming majority of day-to-day LLM use; Claude and ChatGPT are not indoctrinating into the manosphere users asking about discounted cash flow formulae.
(Maybe Grok is though.)
(Context: I asked it to write fake Reddit comments, because I was curious about how realistic they could be. The colorful phrase occurred during its reasoning about the requested subjects.)
You're defining an extremely narrow case and then saying bias is irrelevant within it. At the risk of Godwin's Law that's kind of like saying it's okay if my accountant is a Nazi as long as they only ever have conversations about accountancy.
At the risk of stepping into a hornets nest: is that different than "knowledge"?
Or maybe, what would it mean if an LLM had no social biases? (Would we ever agree that was the case?)
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.
It's not interesting to observe that Grok was successfully trained to be an edgelord; anybody paying attention knew that was easily achievable.
The companies releasing these models actively encourage the act of automated decision making by them. The entire value proposition is the automation of decisions and knowledge work. It's rare to find a use case for them that isn't offboarding your thinking and therefore agency
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints, Zhao et al.
Bias could mean so, so many other things. Was the amyloid hypothesis incorrect? How should we use semicolons? How do you know when meetings waste more time than not? etc. People understand the world via mental shortcuts, via theory-rather-than-fact. We're stuck doing this because we're limited in so many ways. We are so biased about so many things, and this could interact in so many interesting ways. But damned if anyone cares about that. The only thing they seem to care about is how you feel about the "right" or "wrong" groups of people. It's a catastrophic waste of time and energy.
Help me - clearly you understand the truth of the matter far more than those of us who are apparently wasting our time discussing the matter rather than blithely dismissing it. How exactly can you tell that she's a liar who doesn't actually want to end hierarchies? Help us to be as discerning as you are.