Most Users Cannot Identify AI Bias, Even in Training Data

I hope you can see the problem with your very lazy argument.

One only has to see how angry conservatives/musk supporters get at Grok on a regular basis.

> So conservatives might think Fox News is "balanced" and liberals might think it's "far-right"

Article talks like when accidentally the vector for race aligns with emotion so it can classify a happy black personal as unhappy. Just because training dataset has lots of happy white people. It's not about subjective preference

explain how "agreeing" is related

For tech, only Stack Overflow answers modded negatively would 'help'. As for medicine, a Victorian encyclopedia, from the days before germs were discovered could 'help', with phrenology, ether and everything else now discredited.

If the LLM replied as if it was Charles Dickens with no knowledge of the 20th century (or the 21st), that would be pretty much perfect.

Even googling I cannot find a single person claiming that. Not one YT comment. All I can find is liberal outlets/commentors claiming that conservatives believe Fox News is unbiased. There's probably some joes out there holding that belief, but they're clearly not common.

The whole thing is just another roundabout way to imply that those who disagree with one's POV lack critical thinking skills.

In addition bias is not intrinsically bad. It might have a bias toward safety. That's a good thing. If it has a bias against committing crime, that is also good. Or a bias against gambling.

Yeah, confirmation bias is a hell of a thing. We're all prone to it, even if we try really hard to avoid it.

* possible holes in the argument/narrative are presented

* difficult feats like reading minds are admitted to be difficult

* possibly misleading words are hedged

* unimpassioned thought is encouraged

* sources are given (so claims can be checked or researched)

This is all compatible with being totally biased, in the point of view you actually present amid all this niceness. (Expressing fallibility is also an onerous task that will clutter up your rhetoric, but that's another matter.)

Uh, but I could be wrong.

* any facts supporting another view are by definiton biased, and should not be presented

* you have the only unbiased objective interpretation of the minds of the subjects

* you don't bias against using words just because they are hyperbolic

* something unbiased would inevitably be boring, so you need emotional appeal to make anyone care about it

* since no sources are unbiased, identifying any of them would inevitably lead to a bias

https://uclanlp.github.io/corefBias/overview

You can see this with some coding agents, where they are not good at ingesting code and reproducing it as they saw it, but can reply with what they were trained on. For example, I was configuring a piece of software that had a YAML config file. The agent kept trying to change the values of unrelated keys to their default example values from the docs when making a change somewhere else. It's a highly forked project so I imagine both the docs and the example config files are in its training set thousands, if not millions of times, if it wasn't deduped.

If you don't give access to sed/grep/etc to an agent, the model will eventually fuck up what's in its context, which might not be the result of bias every time, but when the fucked up result maps to a small set of values, kind of seems like bias to me.

To answer your question, my gut says that if you dumped a CSV of that data into context, the model isn't going to perform actual statistics, and will regurgitate something closer in the space of your question than further away in the space of a bunch of rows of raw data. Your question is going to be in the training data a lot, like explicitly, there are going to be articles about it, research, etc all in English using your own terms.

I also think by definition LLMs have to be biased towards their training data, like that's why they work. We train them until they're biased in the way we like.

I think there's an example right in front of our faces: look at how terribly SOTA LLMs perform on underrepresented languages and frameworks. I have an old side project written in pre-SvelteKit Svelte. I needed to do a dumb little update, so I told Claude to do it. It wrote its code in React, despite all the surrounding code being Svelte. There's a tangible bias towards things with larger sample sizes in the training corpus. It stands to reason those biases could appear in more subtle ways, too.

If the model is trained on data that shows e.g. that blacks earn less, then it can factually report on this. But it may also suggest this be the case given an HR role. Every solution that I can think of is fraught with another disadvantage.

Except that requires “a level of critical thinking and self-awareness…”

That was oversold though: 1) DEI, in practice, meant attending to a few narrow identity groups; 2) the blind spots of a particular team that need to be covered (more often than not) do not map to the unique perspective of those groups; and 3) it's not practical to represent all helpful perspectives on every team, so representation can't really solve the blind spot problem.

Diversity of thought is more important than superficial diversity which only serves as a proxy for diversity of thought.

I hope the anti DEI movement will not discredit the advantages of diversity itself.

Wut? Completelt unbiased person does not looses ability to see how other make decisions. In fact, when you have less bias in some area, it is super noticeable.

Black people (specifically this means people in the US who have dark skin and whose ancestry is in the US) have a unique identity based on a shared history that should be dignified in the same way we would write about Irish or Jewish people or culture.

There is no White culture, however, and anyone arguing for an identity based on something so superficial as skin colour is probably a segregationist or a White supremacist. American people who happen to have white skin and are looking for an identity group should choose to be identify as Irish or Armenian or whatever their ancestry justifies, or they should choose to be baseball fans or LGBTQ allies or some other race-blind identity.