  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.
