The COMPAS case: an educational journey for explaining fairness in AI-based applications

This article explores the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) case as an invaluable educational resource for elucidating the complexities of fairness assessments in real-world AI applications. The controversial history of this high-profile legal and media case is briefly reviewed, highlighting the multifaceted nature of algorithmic bias in criminal justice. By examining the various analytical approaches proposed to address the COMPAS algorithm’s fairness, this paper underscores the limitations and challenges inherent in each methodology. The case study serves as a compelling illustration of the intricate balance between statistical accuracy, social justice, and ethical considerations in AI-driven decision-making systems. Through this analysis, the article aims to provide students and practitioners with a nuanced understanding of the practical difficulties in achieving and measuring fairness in AI applications.