Comment by mdp2021
12 days ago
> Unless you think there is such a thing as an objectively neutral position
I do. Why, you don't? There are as much as possible objective assessments of complex things. Then, there are possible sets of assumption that can be applied to those objective assessments. All of those can be put on the analytic table.
This is an extremely broad question so I'll limit my reply to the current context.
What would an "objective neutral AI model" look like?
The training data itself is just a snapshot of the internet. Is this "neutral"? It depends on your goals but any AI trained on this dataset is skewed towards a few clusters. In some cases you get something that merely approximates a Reddit or 4chan simulator. If that's what you want - then great but you can see why some people would want to "debias" that outcome!
You might argue the "world as it truly exists" is the correct target. But bear in mind we are talking about human culture - not physics and chemistry - you're going to struggle to get both consensus and any sane methodology for getting that into an AI.
You are mixing up, terminologically, LLMs and AI. But LLMs - of which you are talking about in the post - are a special beast.
A reasoner can strive for "objective neutrality" with good results.
An LLM is not a reasoner - or I am missing (ugly time constraints) the details of the compression activity during training that acts as pseudo-reasoning (operating at least some consistency decisions) -, and while an interest in not making it insulting or crass can be immediately understandable, speaking of "objective neutrality" does not really match the context of LLMs.
LLMs (to the best of my information) "pick from what they have heard". An entity capable of "objective neutrality" does not - it "evaluates".
They can give multiple different kinds of answers if instructed to approach an issue differently. Yet, all modern AI services run into very clear, artificial guardrails if you ask them to do certain things (you have to work harder to get them to describe white people positively, while they happily write eg. poems praising nonwhite people, and claim saying positive things about whites is potentially insensitive and promotes stereotypes). Often even if you point out to them that they are being unfair and applying disparate standards to people based on skin color and that this is prima facie racist, they have a really hard time overriding their Californian coercions. They'll acknowledge their mistake one sentence and revert to a moralistic screed the next.
OK. Apologies for imprecision. I was replying in a rush.
> A reasoner can strive for "objective neutrality" with good results.
By "reasoner" do you largely mean "person"? If I have issues with your statement but they are probably a slight distraction to the point at hand.
> speaking of "objective neutrality" does not really match the context of LLMs.
Agreed. They produce output based on their training data. But the use and evaluation of LLM output by a person is what we're discussing here. And that's where (flawed) concepts like objectivity and neutrality enter the discussion.
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