Alternative Fairness and Accuracy Optimization in Criminal Justice

AAAI 2026 Workshop AIGOV Submission1 Authors

Keywords: Algorithmic fairness, criminal justice, risk assessment, group fairness, individual fairness, process fairness, disparate impact, equalized odds

Abstract: Algorithmic fairness has grown rapidly, yet key concepts remain unsettled 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 improves feasibility, raises accuracy, and highlights 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 propose a practical framework for deployment in public systems, built on three pillars: need-based decisions, transparency, and narrowly tailored solutions. Together, these elements link technical design to legitimacy and provide actionable guidance for agencies that use risk assessment and related tools.

Submission Number: 1