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Making AI predictions fair across all groups—without needing luck

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Imagine a test that's equally unbiased whether you curve it for different school districts or neighborhoods; this is about fixing AI so it stays honest no matter whose outcomes you focus on.

This means machine learning can be both accurate and trustworthy for different communities simultaneously, which matters whenever predictions affect hiring, lending, or healthcare.

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