Learning Optimal Fair Policies

TL;DR

This paper develops a method to learn optimal decision policies that are fair with respect to sensitive attributes, using causal inference and constrained optimization to address biases in data and outcomes.

Contribution

It introduces a novel approach combining causal inference and constrained optimization to learn fair policies with theoretical guarantees, extending prior work on fairness in decision-making.

Findings

The method effectively corrects for biases in synthetic and real criminal justice data.

The approach guarantees fairness constraints are satisfied in the resulting policy.

Empirical results demonstrate improved fairness without sacrificing optimality.

Abstract

Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy. Automated decision procedures and learning algorithms applied to such data may serve to perpetuate existing injustice or unfairness in our society. In this paper, we consider how to make optimal but fair decisions, which "break the cycle of injustice" by correcting for the unfair dependence of both decisions and outcomes on sensitive features (e.g., variables that correspond to gender, race, disability, or other protected attributes). We use methods from causal inference and constrained optimization to learn optimal policies in a way that addresses multiple potential biases which afflict data analysis in sensitive contexts, extending the approach of (Nabi and Shpitser 2018). Our proposal comes equipped with the theoretical guarantee that the chosen fair policy will induce a joint distribution for new instances that satisfies given fairness constraints. We illustrate our approach with both synthetic data and real criminal justice data.