Critical decisions like hiring, college admissions, and loan approvals are guided by predictions made in the presence of uncertainty. While uncertainty imparts errors across all demographic groups, this paper shows that the types of errors vary systematically: Groups with higher average outcomes are typically assigned higher false positive rates, while those with lower average outcomes are assigned higher false negative rates. We characterize the conditions that give rise to this disparate impact and explain why the intuitive remedy to omit demographic variables from datasets does not correct it. Instead of data omission, this paper examines how data enrichment can broaden access to opportunity. The strategy, which we call "Affirmative Information," could stand as an alternative to Affirmative Action.

翻译：在招聘、大学录取和贷款审批等关键决策中，预测结果往往伴随着不确定性。尽管不确定性在所有人口群体中都会导致误差，但本文表明，错误类型存在系统性差异：平均结果较高的群体通常被赋予更高的假阳性率，而平均结果较低的群体则被赋予更高的假阴性率。我们描述了产生这种差异性影响的条件，并解释了为什么从数据集中省略人口统计变量的直观补救措施无法纠正这一问题。与数据省略不同，本文探讨了数据丰富化如何拓宽机会获取渠道。我们将这一策略称为“平权信息”，它可作为平权行动的替代方案。