There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not. 1 Northpointe's algorithm had differing Type-1 and Type-2 error rates across the two groups. 2 Roughly, the accuracy of COMPAS scores was the same for both groups at all risk levels.
REF study a model of criminal justice in which crime rates are responsive to the classifiers used to determine criminal guilt, and study which fairness constraints are consistent with the goal of minimizing crime.
Fair Prediction with Endogenous Behavior
ArXiv
ArXiv
Psychology Economics Computer Science