Alternative Fairness and Accuracy Optimization in Criminal Justice

The study on algorithmic fairness in criminal justice highlights the ongoing challenges in achieving equitable outcomes through technology. By reviewing group, individual, and process fairness, the authors identify conflicts and propose a modification that focuses on minimizing weighted error loss while permitting small tolerances in false negative rates. This nuanced approach not only aims to enhance predictive accuracy but also surfaces ethical considerations regarding error costs. The critique of current methods, which often rely on biased data and face issues like latent affirmative action, underscores the need for a more robust framework. The authors suggest a practical deployment strategy based on need-based decisions, transparency, and narrowly tailored solutions, linking technical design to legitimacy. This research is significant as it provides actionable guidance for agencies utilizing risk assessment tools, potentially leading to fairer outcomes in the criminal justice syste…