Fairness in Criminal Justice Risk Assessments: The State of the Art

TL;DR

This paper clarifies the complex tradeoffs between different fairness notions and accuracy in criminal justice risk assessments, highlighting the inherent conflicts and practical challenges in achieving fairness.

Contribution

It provides an integrated analysis of fairness and accuracy, identifying multiple fairness types and illustrating their incompatibilities through empirical data.

Findings

Six types of fairness identified, some incompatible with each other.

Maximizing both fairness and accuracy simultaneously is generally impossible.

Different base rates across groups complicate fairness in practice.

Abstract

Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments. Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy. Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging tradeoffs.