2024/02/19 by Marcello Di Bello, Di Bello, Marcello, Nicolò Cangiotti +3
Social Sciences · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Data Structures and Algorithms (cs.DS) #Digitalization, Law, and Regulation #Ethics and Social Impacts of AI #European Criminal Justice and Data Protection #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2402.12062
openalex publication_date 2024/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
By combining the philosophical literature on statistical evidence and the interdisciplinary literature on algorithmic fairness, we revisit recent objections against classification parity in light of causal analyses of algorithmic fairness and the distinction between predictive and diagnostic evidence. We focus on trial proceedings as a black-box classification algorithm in which defendants are sorted into two groups by convicting or acquitting them. We defend a novel principle, causal equal protection, that combines classification parity with the causal approach. In the do-calculus, causal equal protection requires that individuals should not be subject to uneven risks of classification error because of their protected or socially salient characteristics. The explicit use of protected characteristics, however, may be required if it equalizes these risks.