Social credit systems benefit minorities and reduce race-based discrimination. Yet another case where the ignorant beliefs of baizhou (whiny first world whites) harm social justice while claiming to support it.https://twitter.com/narayanarjun/status/1056920436959789062 …
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Replying to @patrissimo
I see where you are coming from (and may even agree in the case of free countries) but evidence suggests that ethnic, sexual, and religious minorities as well as political dissidents will suffer greatly under the Chinese dictatorship’s new social credit systems.
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Replying to @gladstein
Sure, there may be country-specific factors that lead to that. But most Westerners seem to be reacting against the idea as dystopic on its face, not realizing that data-based social credit actually counteracts the pervasive racism that stems from humans' intuitive stereotyping.
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Replying to @0x6c6f6d @gladstein
If stereotypes are wrong, a data-based approach will reveal that. Whereas we know our human brains tend to crudely stereotype, right or wrong.
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Replying to @patrissimo @gladstein
People won't stop stereotyping because some government score tells them the person is fine. The deeply entrenched stereotypes you refer to are mitigated over multiple generations. I'm skeptical you can hasten that process by using a score.
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Or that a score would be more efficient than a suit. Many reasons. The first one is that there is a mismatch between your expectation that people will work to correct their biases and the reality, that people will rationalize them until repeated positive experience corrects it.
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"This fellow has a high score because of the affirmative action", "That fellow has a high score because this is a fellow-owned bank and they control the world" etc. no way the state has enough trust capital to have the citizenry rely on it, it will have to be through coercion
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If an Uber driver sees your score and not your picture...seems like that works. I think people's stereotypes (racist for example) are mild, and cause harm in aggregate. But because they are mild, they are easy to override with data.
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