Alternative Fairness and Accuracy Optimization in Criminal Justice

TL;DR

This paper proposes a flexible fairness-accuracy trade-off method for criminal justice algorithms, emphasizing ethical error cost considerations and practical deployment frameworks to improve legitimacy and transparency.

Contribution

It introduces a modified fairness approach that relaxes parity constraints, enhancing accuracy and ethical error management in criminal justice algorithms.

Findings

Improved predictive accuracy with fairness constraints

Enhanced ethical error cost management

Practical deployment framework for public decision systems

Abstract

Algorithmic fairness has grown rapidly as a research area, yet key concepts remain unsettled, especially in criminal justice. We review group, individual, and process fairness and map the conditions under which they conflict. We then develop a simple modification to standard group fairness. Rather than exact parity across protected groups, we minimize a weighted error loss while keeping differences in false negative rates within a small tolerance. This makes solutions easier to find, can raise predictive accuracy, and surfaces the ethical choice of error costs. We situate this proposal within three classes of critique: biased and incomplete data, latent affirmative action, and the explosion of subgroup constraints. Finally, we offer a practical framework for deployment in public decision systems built on three pillars: need-based decisions, Transparency and accountability, and narrowly tailored definitions and solutions. Together, these elements link technical design to legitimacy and provide actionable guidance for agencies that use risk assessment and related tools.