Privilege Scores

2025-02-03

AI Summary

This paper addresses implicit unfairness in machine learning systems arising from protected attributes (e.g., gender, race). We propose the Privilege Score (PS)—a causal counterfactual metric quantifying prediction disparities between the real world and a counterfactual “fair world” where attribute influence is eliminated—applicable at both individual and group levels. We formally define and estimate attribute-driven non-neutrality for the first time. Our framework introduces PS and its decomposition into Privilege Score Components (PSCs), enabling individual-level bias mitigation and global policy interpretation, with statistically grounded confidence intervals. The method integrates causal modeling, counterfactual prediction, feature contribution attribution, and uncertainty quantification. Evaluated on real-world mortgage approval and university admissions datasets, PS successfully identifies gender- and race-based privilege patterns, uncovers underlying bias mechanisms, and demonstrates robustness, interpretability, and practical policy relevance.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Bias-transforming methods of fairness-aware machine learning aim to correct a non-neutral status quo with respect to a protected attribute (PA). Current methods, however, lack an explicit formulation of what drives non-neutrality. We introduce privilege scores (PS) to measure PA-related privilege by comparing the model predictions in the real world with those in a fair world in which the influence of the PA is removed. At the individual level, PS can identify individuals who qualify for affirmative action; at the global level, PS can inform bias-transforming policies. After presenting estimation methods for PS, we propose privilege score contributions (PSCs), an interpretation method that attributes the origin of privilege to mediating features and direct effects. We provide confidence intervals for both PS and PSCs. Experiments on simulated and real-world data demonstrate the broad applicability of our methods and provide novel insights into gender and racial privilege in mortgage and college admissions applications.

Problem

• Machine Learning
• Unfair Bias
• Social Equity

Innovation

• Privileged Score
• Fairness in Machine Learning
• Bias Mitigation