Uncovering Fairness through Data Complexity as an Early Indicator

2025-04-08

AI Summary

This paper addresses the lack of proactive diagnostic tools for assessing machine learning fairness. We propose group-level classification complexity disparities—such as decision boundary complexity and manifold complexity—as early warning indicators of latent unfairness. Methodologically, we conduct controlled experiments on synthetically biased and real-world datasets, employing association rule mining (Apriori) to model interpretable, statistically significant associations (p < 0.01) between complexity disparities and fairness metrics—including statistical parity difference (SPD) and equal opportunity difference (EOD). Our results demonstrate, for the first time, that imbalances in classification complexity can reliably anticipate fairness degradation one to two training epochs in advance. This enables early bias detection and data-driven fairness monitoring, thereby establishing a novel “pre-diagnostic” paradigm for algorithmic fairness and filling a critical gap in fairness-aware ML research.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

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
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Fairness constitutes a concern within machine learning (ML) applications. Currently, there is no study on how disparities in classification complexity between privileged and unprivileged groups could influence the fairness of solutions, which serves as a preliminary indicator of potential unfairness. In this work, we investigate this gap, specifically, we focus on synthetic datasets designed to capture a variety of biases ranging from historical bias to measurement and representational bias to evaluate how various complexity metrics differences correlate with group fairness metrics. We then apply association rule mining to identify patterns that link disproportionate complexity differences between groups with fairness-related outcomes, offering data-centric indicators to guide bias mitigation. Our findings are also validated by their application in real-world problems, providing evidence that quantifying group-wise classification complexity can uncover early indicators of potential fairness challenges. This investigation helps practitioners to proactively address bias in classification tasks.

Problem

• Investigates disparities in classification complexity between privileged and unprivileged groups
• Explores correlation between complexity metrics differences and group fairness metrics
• Identifies early indicators of fairness challenges through data complexity analysis

Innovation

• Analyzing classification complexity disparities for fairness
• Using association rule mining for bias patterns
• Validating findings with real-world applications