Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations

📝 Original Info

• Title: Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations
• ArXiv ID: 2512.23769
• Date: 2025-12-29
• Authors: Ranit Debnath Akash, Ashish Kumar, Verya Monjezi, Ashutosh Trivedi, Gang, Tan, Saeid Tizpaz-Niari

📝 Abstract

Fairness in algorithmic decision-making is often framed in terms of individual fairness, which requires that similar individuals receive similar outcomes. A system violates individual fairness if there exists a pair of inputs differing only in protected attributes (such as race or gender) that lead to significantly different outcomes-for example, one favorable and the other unfavorable. While this notion highlights isolated instances of unfairness, it fails to capture broader patterns of clustered discrimination that may affect entire subgroups. We introduce and motivate the concept of discrimination clustering, a generalization of individual fairness violations. Rather than detecting single counterfactual disparities, we seek to uncover regions of the input space where small perturbations in protected features lead to k-significantly distinct clusters of outcomes. That is, for a given input, we identify a local neighborhood-differing only in protected attributes-whose members' outputs separate into many distinct clusters. These clusters reveal significant arbitrariness in treatment solely based on protected attributes, exposing patterns of algorithmic bias tha...

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