Fairness-Aware Classification Based on Rawlsian Veil of Ignorance: A Mathematical Framework for Bias Detection and Mitigation in Machine Learning

Abstract

Machine learning's penetration into high-stakes decision-making—credit approvals, healthcare triage, criminal risk assessment—has amplified pre-existing societal inequities rather than ameliorating them. This study operationalizes John Rawls's "veil of ignorance" (1971) as a computational principle for binary classifiers, confronting a gap: most fairness metrics lack philosophical grounding while Rawlsian theories remain mathematically unformalized. Through three empirical phases—(1) baseline logistic regression on full feature sets, (2) bias quantification via disaggregated metrics across protected groups, and (3) mitigation via pre-processing blindess and post-processing threshold optimization—we demonstrate how ignorance of demographic attributes can be algorithmically imposed. Using the German Credit Dataset (n=1,000), we expose a 12.9% accuracy gap between gender groups in standard models. Our framework collapses demographic parity difference from 15.7% to 0.1% while paradoxically boosting accuracy by 2.8% (from 72.0% to 74.0%), challenging the fairness-accuracy sacrifice orthodoxy. Counterintuitively, naive feature removal worsened bias (+8.9%), only proxy-aware pruning achieved DPD reduction of 32.6%. These findings suggest that Rawlsian principles, when translated into constrained optimization, yield Pareto-superior solutions—though we argue such technical fixes must complement, not substitute for, institutional reform.