On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems

• URL: http://arxiv.org/abs/2509.25662v1
• Date: Tue, 30 Sep 2025 01:58:59 GMT
• Title: On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems
• Authors: Belona Sonna, Alban Grastien,
• Abstract summary: We propose a novel framework using formal abductive explanations to explain proxy discrimination in individual AI decisions.<n>Our method identifies which features act as unjustified proxies for protected attributes, revealing hidden structural biases.<n>As a proof of concept, we showcase the framework with examples taken from the German credit dataset.
• Score: 5.220940151628734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) systems in high-stakes domains raise concerns about proxy discrimination, unfairness, and explainability. Existing audits often fail to reveal why unfairness arises, particularly when rooted in structural bias. We propose a novel framework using formal abductive explanations to explain proxy discrimination in individual AI decisions. Leveraging background knowledge, our method identifies which features act as unjustified proxies for protected attributes, revealing hidden structural biases. Central to our approach is the concept of aptitude, a task-relevant property independent of group membership, with a mapping function aligning individuals of equivalent aptitude across groups to assess fairness substantively. As a proof of concept, we showcase the framework with examples taken from the German credit dataset, demonstrating its applicability in real-world cases.

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