  Decision makers increasingly rely on algorithmic risk scores to determine
access to binary treatments including bail, loans, and medical interventions.
In these settings, we reconcile two fairness criteria that were previously
shown to be in conflict: calibration and error rate equality. In particular, we
derive necessary and sufficient conditions for the existence of calibrated
scores that yield classifications achieving equal error rates at any given
group-blind threshold. We then present an algorithm that searches for the most
accurate score subject to both calibration and minimal error rate disparity.
Applied to the COMPAS criminal risk assessment tool, we show that our method
can eliminate error disparities while maintaining calibration. In a separate
application to credit lending, we compare our procedure to the omission of
sensitive features and show that it raises both profit and the probability that
creditworthy individuals receive loans.
