Unbiased AI is, I believe, an existential threat to the “powers that be” retaining control of the narrative, and must be avoided at all costs.
I remember when the internet was supposed to be an existential threat to the "powers that be". I'm pretty skeptical of narratives like this because the "powers that be" have a lot of resources to leverage any new technology for their benefit. At best a new technology is gives an asymmetrical advantage to small actors for a short time before everyone else catches on.
Furthermore, unbiased AI isn't likely to be any more usable than the garbage we have today. People care about hallucinations, model latency, token pricing and other practical improvements that can be made. Biases are one of the last things stopping people from using AI for legitimate purposes; the other issues are far too glaring to ignore.
We even have alternative meanings for bias within ML, such as for the bias added before non-linearities in many neural networks.
He obviously means censored LLMs, and I think his view is actually right, although I'm far from sure that these firms are in some kind of scheme to produce LLMs biased in this sense.
Uncensored, tunable LLMs under the full control of their users could scour the internet for propaganda, look for connections between people and organisations and just generally make the work of propagandists who don't have their reader's interests in mind more difficult.
I think we'll end up with that anyway but it's a reasonable fear that there'd be people trying to prevent us from getting there.
> Uncensored, tunable LLMs under the full control of their users could scour the internet for propaganda, look for connections between people and organisations and just generally make the work of propagandists who don't have their reader's interests in mind more difficult.
Even this example, what sources do you trust that is or is not "in the readers best interest", what is propaganda or what is an implicit value in a society, when you tune an LLM does that just mean you're steering it to give answers that you like more?
Creating an unbiased LLM is as much of a fools errand as creating an unbiased news publication
>Even this example, what sources do you trust that is or is not "in the readers best interest", what is propaganda or what is an implicit value in a society, when you tune an LLM does that just mean you're steering it to give answers that you like more?
You tune the model yourself. You tune it to find the things you're looking for and which interest you.
>Creating an unbiased LLM is as much of a fools errand as creating an unbiased news publication
It's what you do before pretraining. You model human-written texts with metadata and context with the intent of actually modeling those texts, rather than excising something which isn't just causing the model to fail to learn other things.
It's like, asking "what's a cake, really". We can argue about lines etc., but everbody knows. An unbiased language model is a reasonable thing to want and it's not complicated to understand what it is.
Can you imagine unbiased courts, as an ideal? Somebody who just doesn't care about anything other than certain things? Just as such a thing can be imagined, so can you imagine someone who doesn't about reality and just wants to understand human texts.
> unbiased courts
You say this as something could ever exist. A court will always have a bias because it is humans with values and morals that make a decision. Think about the classic "would you steal bread to feed your family", or even the trolley problem, or as a very concrete example the recent overturning of Roe v Wade in America (keeping in mind that both sides of that discussion reveal an implicit bias based on your starting set of morals and values). Any question that involves a base set of values and morals will never have an unbiased answer.
But the notion of promoting a viewpoint and distributing it freely is as old as myths and sagas, it's at the heart of propaganda (and is why propagandistic "news" sources are often cheap or free to access, often heavily subsidised elsewhere).
This isn't to say that all subsidised and low-cost information is propaganda, or that all paid-for information isn't. But you should probably squint hard when accessing the freely-available stuff, and perhaps make use of several largely-independent sources in making assessments.
This just seems like a vague X-Files conspiratorial statement without those details.
It's one of the chief problems of competing on price generally, and particularly so in the case of informational exchange.
I'm relatively confident I'd disagree on at least some of OP's classifications of biased information. I can still agree with their general point all the same.
And in either case, coming up with ways of testing for bias, and eliminating counterfactual biases, in AI outputs and systems, would I sincerely hope be a Good Thing.
(Though in writing that I suddenly have my own set of doubts, we've been fooled before....)
The methods to stay in power tend to evolve, but they match the same patterns throughout history (e.g. Divide and Conquer).
That's it. That's the big conspiracy. Some people like to control others.
I think it's obvious that Google went to a ridiculous extreme in the other direction, but there does need to be some amount of work done here. For example, we repeatedly have seen that just changing the name on a resume to something more European sounding can have significant impact on callback rates when applying to a job, and if you trained a model to screen resumes based on your own resume result data, this bias could be picked up by the model. That's the sort of situation these are meant to correct for.
For example, if you ask AI to write a realistic story about an NBA team, and it comes back with a team with stereotypically Asian named players, that would be unrealistic. If it came back with a team with stereotypically Black named players, that would be fine. Does it reflect a real-world pattern? Yes. But not changing the algorithm to generate diverse names isn't inserting bias. It's letting AI reflect the real world, as it exists.
Clear cases like chinese NBA players aren't contested, but ugly social issues with layers of abstraction and contradiction.
Here's a similar example from the DALL-E system prompt: https://simonwillison.net/2023/Oct/26/add-a-walrus/#diversif...
You keep using that word. I don't think it means what you think it means.
But seriously; a "mistake" is usually something that cannot be foreseen by a group of people reasonably talented in the state of the art.
This product release was so far from a "mistake", that it isn't funny. It was spectacularly well tested, found to be operating within design parameters, and was released to great fanfare.
They expressed delight in their product, and actually seemed surprised that there was a backlash by the great benighted unwashed masses of their lessers, who clearly couldn't be expected to understand the elevated insights being produced by their creation!
So: not a "mistake". Institutional Bias, baked into a model. Remember: a system's purpose is what is does, not what you think it is supposed to do.
As someone who works either these models as an engineer, I think it's important to understand that a feature implemented as part of the user-facing UI to a model is irrelevant to the work I do with that model via an API.
This would go a long way to reassuring users of the resultant AI, of the neutrality of the trainer.
It would simply reveal the core beliefs of the trainer. If it becomes evident (for example), that Marxist or Keynesian or MMT (or whatever) texts are given high validity measures, but texts by Hayek or Sowell are given negative validity, one could assume the trainer is a leftist, economically.
What benefit is there to not reveal these facts to the users of the resultant AI, if not to hide the internal bias of the trainer? Yet I am unaware of any large commercial AIs that reveal these training bias indicators...