  Algorithmic risk assessments hold the promise of greatly advancing accurate
decision-making, but in practice, multiple real-world examples have been shown
to distribute errors disproportionately across demographic groups. In this
paper, we characterize why error disparities arise in the first place. We show
that predictive uncertainty often leads classifiers to systematically
disadvantage groups with lower-mean outcomes, assigning them smaller true and
false positive rates than their higher-mean counterparts. This can occur even
when prediction is group-blind. We prove that to avoid these error imbalances,
individuals in lower-mean groups must either be over-represented among positive
classifications or be assigned more accurate predictions than those in
higher-mean groups. We focus on the latter condition as a solution to bridge
error rate divides and show that data acquisition for low-mean groups can
increase access to opportunity. We call the strategy "affirmative information"
and compare it to traditional affirmative action in the classification task of
identifying creditworthy borrowers.
