Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Sets

Understand

Risk assessment algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs.

• In this paper, we adopt a framework from conformal prediction sets to remove unfairness from risk algorithms themselves and the covariates used for forecasting.
• From a sample of 300,000 offenders at their arraignments, we construct a confusion table and its derived measures of fairness that are effectively free any meaningful differences between Black and White offenders.
• We also produce fair forecasts for individual offenders coupled with valid probability guarantees that the forecasted outcome is the true outcome.