The more likely you are statistically to have some feature, the less likely it should be for other people to believe that you have that feature. Bollinger and many others like her argue that it's possible for a statistical generalization to be both accurate and unjust. So then members of that group face just a higher risk of suffering the false positive. And that's a fairness-based consideration against using this kind of evidence.

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