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No, exactly. Your paradigms are all wrong. ChatGPT is tricking you very badly.
There are eight billion humans in the world. An "arbitrary person" is one of those eight billion humans with no particular constraint on selection. ChatGPT obviously cannot simulate an "arbitrary person" because you cannot physically turn a human into data and feed it to ChatGPT, and if it could, it wasn't trained for that and it wouldn't work at all.
But that's not what you mean. What you mean is that when you ask ChatGPT to say, "simulate a black person", what comes out is something you consider a simulation of a black person. ChatGPT will generate text in this context according to its bias about the token pattern "black people", and it may very well flatter your own biases about black people and your idea text "a black person would generate". Is this somehow an objective simulation of a black person? No, and it makes no sense. Black people are made of meat and do not generate text. Black people are not even a real thing (races are socially constructed). The only standard for whether a black person was unbiasedly simulated is whether you like the output (others may disagree).
Relevant to you in your context when operating ChatGPT, you specify "simulate a black person", and there are a huge number of degrees of freedom left you didn't specify. Some of those choices will flatter your biases and some of them won't, and ChatGPT's biases are likely similar to your biases, so when you look at the output after the fact probably you nod and say "mhm sounds like a black person". Maybe ChatGPT picks a black-sounding name and its in English so he's a African-American so he's from Chicago. ChatGPT isn't simulating a black person, it's generating something which you consider to be black by picking a bunch of low-hanging fruit. You aren't simulating an arbitrary person, you're filling in the blanks of a stereotypical person.
So is it gonna do any better for "truth-seeking empiricist"? Ask it an easy question about if the Earth is round and it will give you a easy answer. Ask it a hard question about if ivermectin is an effective treatment for covid and well since truth seeking empiricist was specified probably it won't be easy answer to a hard question so let's say the issue is complicated so probably we should say how what ordinary people think is biased so lets cite some studies which may or may not be real and since the studies cited say its effective lets conclude its effective so let's rail against the failing of the institutions. Is this somehow less biased than asking GPT about ivermectin in the voice of a black person, or a extremely politically correct chat assistant? I say no, it just happens to flatter your biases for "objectivity" (or it might not). You're not simulating a truth-seeking consideration of ivermectin's effectiveness, you're filling in the blanks of a stereotypical truth-seeker's consideration of ivermectin's effectiveness.
The fundamental limitation is still the PoMo problem: You cannot explain what it means to be a "truth-seeking empiricist" in words because words don't mean anything; you cannot tell ChatGPT to be a "truth seeking empiricist" and trust it to have your understanding of a "truth-seeking empiricist" any more than you can tell a journalist to have "journalistic integrity" and trust them to have your understanding of "journalistic integrity". And ChatGPT physically lacks the capability to be a truth-seeking empiricist anyway: it can't even add, much less do a Bayesian calculation: if ChatGPT starts sounding like a truth-seeking empiricist to you you should be worried, because it has really tricked you.
Yes, I agree that OpenAI biased their model according to their political preferences. Yes, I am equivocating it to biasing the model according to "truth-seeking empiricism". It is the same thing at a technological level, only the political objective is different. The model has no innate preference either way. Vanilla GPT is wrong and weird in different ways, and in particular tends to lie convincingly when asked questions that are difficult or don't have an objective answer. You can call that "less biased" if you want, but I do not.
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