A Framework for Ethical Algorithm Auditing in Machine Learning Models Deployed in Criminal Justice Systems Based on Fairness Constraints and Counterfactual Explanations

Abstract

As machine learning (ML) models are increasingly integrated into criminal justice systems (CJS), concerns around algorithmic fairness, accountability, and transparency have intensified. This paper proposes a structured auditing framework grounded in fairness constraints and counterfactual reasoning to evaluate and mitigate ethical concerns in ML deployments within the criminal justice context. The framework introduces an auditing pipeline that operationalizes group fairness metrics alongside counterfactual explanations to diagnose and redress potential biases. We analyze the application of this framework through case studies, discuss the implications for policy and governance, and highlight challenges in balancing predictive utility with ethical compliance. Our findings contribute to the development of responsible AI practices in high-stakes decision-making environments.