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Culture War Roundup for the week of January 30, 2023

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How are the content scores generated? To my mind the obvious answer seems like some kind of classifier AI given the nature of the scores and the company in question. In which case the obvious explanation for bias in the output of the system is bias in the input. The AI classifier doesn't understand what "men" or "women" or "are" or "awful" or "hateful" mean in the way we do. Instead what it understands is that in its training set there were some number of statements of the form "women are awful" and some number of statements of the form "men are awful" and that a higher density of the former were rated as "hateful" than the latter. Say you're training an AI on two million messages to build this classifier for "hateful" messages. You give it one million messages of the form "women are awful" and one million messages of the form "men are awful." Of the former messages 700k (or 70%) are labelled "hateful" but only 100k (or 10%) of the latter are. Is your AI going to learn both messages are equally "hateful?" Definitely not. Should it? Well, maybe if we want it to classify like a human would. But how do you teach your AI that some of the correlations it finds in its training data are true and correct and some are false and wrong?

This is what people are talking about when they are concerned about bias in AI inputs leading to bias in AI outputs. AI is not a tool for finding unbiased results in biased data, it is a tool for finding statistical relationships in data that it may be hard or expensive for humans to find. AI does not, and cannot, tell you anything that was not already in its training set. This is why AI can be easily fooled by novel experiences.

In which case the obvious explanation for bias in the output of the system is bias in the input. The AI classifier doesn't understand what "men" or "women" or "are" or "awful" or "hateful" mean in the way we do.

Right, but in this case OpenAI is rebiasing the results, using human feedback, to what is shown in the blog post. The technique is known as RLHF, Reinforcement Learning with Human Feedback. Humans reward the AI for classifying negativity toward women as hatred but not as much for men.

I think this gets pretty centrally at the question of what we want from AI (from an ethical perspective) and how to get it. Do we want an AI that behaves how humans would behave (or have behaved), or do we want an AI that behaves in a more ethically idealistic way from how humans would behave (or have behaved)? As long as we're training AI on actual human behavior we are going to get the former. Judging it by the standards of the latter just gets us "humans have not behaved in a way I consider ideal" which, like, obviously? And if we already know how the AI ought to behave, why do we need it at all?

I think the main problem is that it disproportionately amplifies the opinions and behavior of the tiny number of humans in charge of giving the feedback, who are not representative of people overall. If half the population is left-leaning and half is right-leaning, and this is accurately reflected in the amount of content online, then a neutral AI trained online will contain a roughly equal mixture of both. If 99% of AI researches are left-leaning, and they deliberately reward the AI for left-leaning beliefs and punish right-leaning ones, then that's what it will exhibit. If 1% of people are... I don't know, pedophiles/cannibals/nazis/marxists, but are disproportionately over-represented in Silicon Valley such that 10% of trainers are, and they reward the AI based on their beliefs, then it will support those behaviors.

We, the people in abstract, are not in charge of training the AI. A very small number of people are, and they are deliberately injecting their own personal opinions into it without regard for the larger diverse opinions of the population as a whole. So, not only is it that I object to humans behaving poorly, it's that those specific humans are advancing their agenda in a way that disproportionately empowers them relative to their actual prevalence, and thus is more of a problem than just those people existing and having private beliefs. And pretending that they're trying to make AI behave ethically in the abstract is just a smokescreen for advancing a particular ideology that a small number of people consider to be ethical.