🤖 AI Summary
This work addresses a critical gap in algorithmic fairness by observing that existing fairness metrics predominantly focus on disparities in model outcomes across groups while overlooking biases inherent in the explanation process itself. To bridge this gap, the authors propose Group-wise Explanation Stability Discrepancy (GESD), an explainer-agnostic and model-agnostic, procedure-oriented fairness metric. They further introduce the Fairness–Explanation–Utility (FEU) framework, which jointly optimizes predictive utility, outcome fairness, and explanation fairness through multi-objective optimization and explainability techniques. This approach is the first to extend fairness evaluation to the level of explanations, enabling comparative analysis of explanation stability across demographic groups. Empirical results demonstrate that GESD effectively captures group-based disparities in explanation quality, and the FEU framework consistently achieves superior trade-offs between prediction accuracy and fairness across multiple benchmark datasets compared to state-of-the-art methods.
📝 Abstract
Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predictions. While existing fairness metrics (e.g., statistical parity, equal opportunity) effectively quantify outcome-oriented disparities, they offer limited insight into the procedure or explanation behind biased decisions. To address this gap, we propose Group-level Explanation Stability Disparity (GESD), a \textit{procedural-oriented} fairness metric that measures disparities in the stability, robustness, and sensitivity of model explanations across different subgroups in a protected category. %GESD is explainer-agnostic, model-agnostic, and extends the scope of fairness analyses to the level of explainability. We further integrate GESD into a multi-objective optimization framework that jointly optimizes for utility, outcome-based fairness, and explanation-based fairness called FEU (Fairness--Explainability--Utility). Empirical results on multiple benchmark datasets show that GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods. By bridging outcome-based and explanation-based fairness, GESD offers a comprehensive tool for diagnosing and mitigating bias in predictive modeling. Our code and datasets are available on GitHub {\hyperlink{https://github.com/horlahsunbo/GESD}{https://github.com/horlahsunbo/GESD}}