For me personally, I consider prejudiced action to be 'bad' when it fails to update based on countering evidence, and 'fine' if it does update. SOME EXAMPLES:
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Is it good or bad if your risk assessment greatly exceeds (or falls short of) the actual risk posed by a person who represents a particular demographic group? (e.g. to be *very* afraid of all men, despite knowing only a small percentage act violently.)
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How would you see that being good?
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Totally agree. Stereotypes are pooled experience disseminated as a meme across culture - of course it won't apply to everyone, but they are useful until they are not (i.e. evidence to the contrary) - 'knowledge' should always be contextualized/updated in favor of tabula rasa
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That's not true though, stereotypes aren't just pooled experience, they're cultivated and engineered narratives produced by media. None of us have ever seen a Chinese person eat a dog, but we all know the stereotype. It has nothing to do with actual experiences.
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What about concerns of the form "Even when I think they're safe, ___s are still more likely to harm me than non-____s". That meta-level principle may be evidentially justified, but it undermines the case-by-case updates you want.
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But they're not seperate right? We are social creatures so couldn't acting on statistical analysis, impact your statistics? That is to say, if everyone keeps walking away from Joe, fearful of Joe, not open to Joe, wouldn't that impact Joe? Couldn't it even make Joe TURN scary?
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