On the Robustness of Fairness Practices: A Causal Framework for Systematic Evaluation

2026-01-07

arXiv.org

AI Summary

This study addresses the lack of systematic evaluation of robustness in existing fair machine learning methods under realistic data perturbations such as label noise, missing data, and distribution shifts. It introduces a causal inference framework to conduct the first comprehensive robustness analysis of mainstream fairness interventions—including sensitive attribute handling and bias mitigation techniques—under non-ideal data conditions. Empirical results demonstrate that several widely used approaches suffer significant performance degradation under common perturbations, thereby exposing critical limitations for real-world deployment. These findings provide both theoretical grounding and practical guidance for developing more reliable and robust fair machine learning systems.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Machine learning (ML) algorithms are increasingly deployed to make critical decisions in socioeconomic applications such as finance, criminal justice, and autonomous driving. However, due to their data-driven and pattern-seeking nature, ML algorithms may develop decision logic that disproportionately distributes opportunities, benefits, resources, or information among different population groups, potentially harming marginalized communities. In response to such fairness concerns, the software engineering and ML communities have made significant efforts to establish the best practices for creating fair ML software. These include fairness interventions for training ML models, such as including sensitive features, selecting non-sensitive attributes, and applying bias mitigators. But how reliably can software professionals tasked with developing data-driven systems depend on these recommendations? And how well do these practices generalize in the presence of faulty labels, missing data, or distribution shifts? These questions form the core theme of this paper.

Problem

• fairness
• machine learning
• robustness
• distribution shift
• bias

Innovation

• causal framework
• fairness robustness
• bias mitigation
• distribution shift
• systematic evaluation