Recidivism prediction instruments (RPI's) provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This paper discusses several fairness criteria that have recently been applied to assess the fairness of recidivism prediction instruments. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when a recidivism prediction instrument fails to satisfy the criterion of error rate balance.
Motivated by the controversy of the potential discriminatory bias in recidivism prediction instruments, REF showed an intrinsic incompatibility between equalized odds and predictive rate parity.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Big data
Big data
Mathematics Computer Science Medicine