It's almost as though bias-making machinery is embedded in the texts these things are trained on.
It's wild to see quantitative researchers catching even just a glimpse of what culture/media/literary theorists have been swimming in for decades.
For a while (It's getting better with Astra, but still there), a lot of these models would "accuse" you of wishing that magic existed or something, and constantly drawing distinctions to try and "prove" something that nobody ever said.
I think that holding and generating distinctions, when it comes to problem solving, is a very powerful tool. If nothing else, it's a way to force yourself to be adversarial. Conflation is a "damning" operation, while distinctions will at most blow up your search complexity (which, we know from computer science, isn't free, but still).
But it's not a way to build a model, a theory, a society. It's like permanently being the "uhm, actually" redditor.
From 2015: "We investigate the impact of seller race in a field experiment involving baseball card auctions on eBay. Photographs showed the cards held by either a darkskinned/African-American hand or a light-skinned/Caucasian hand. Cards held by African-American sellers sold for approximately 20% ($0.90) less than cards held by Caucasian sellers, and the race effect was more pronounced in sales of minority player cards. "
> As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased
https://smalldocs.org/s/6kEgfy54oclH4KR9HX847w#k=ywVL86PcTCo...
It's an interesting result (agents develop biases in their context) which reflects a lot of my experience working with agent, where I observe a lot of, what I kind of call, "context nudging" - where a droplet of an idea in an agent's context pushes its direction/output significantly. When it happens to me it always makes me question the type of intelligence LLMs provide.
[I am the developer behind SmallDocs. Source: https://github.com/espressoplease/smalldocs]
Is this a joke?
I used to despise this kind of thing but it sheds light on the enormous generalization problems that aren't even close to being solved.
Rather than solve the problem of "why does LLM output slightly stratify between Tufa and Weki like this", I would just not conduct my hiring using this paper's methodology.
Helping regional warlords run clan-aware conscription drives is AI safety research now.https://openreview.net/attachment?id=pc7fqaOcAH&name=origina...
The LLMs take in text which conditions their output. That means even nonsense text - such as a "tribal affiliation" to a tribe that may not have ever existed - ALSO condition the output, because the tribe name is a token in the context window and there's no such thing as a perfectly neutral token.
Taking away the race/ethnicity layer for a moment, it might be that an LLM develops a predisposition to emit positive terms (like "accept") when the prompt contains "banananow", and negative terms when it contains "pearian". That's the very definition of bias, and hacking those biases could give individuals serious socioeconomic benefits!
Unfortunately IRL there are lots of signals about a person's heritage encoded into things like their name or what school they went to. You would need to filter all of those signals out to have properly race-blind hiring.
So in the end these signals are going to make it into the AI and the question is whether the AI is going to pick up on those signals and use them when making decisions.