Fair Prediction with Endogenous Behavior

• URL: http://arxiv.org/abs/2002.07147v1
• Date: Tue, 18 Feb 2020 16:07:25 GMT
• Title: Fair Prediction with Endogenous Behavior
• Abstract summary: Using criminal justice as an example, we study a model in which society chooses an incarceration rule. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate.
• Score: 14.004287903552534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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