MESD: Detecting and Mitigating Procedural Bias in Intersectional Groups
This work addresses an important gap in fairness research by examining explainability across multiple protected categories, though it appears incremental as it builds on existing fairness and explainability concepts.
The authors tackled the problem of procedural bias in machine learning models by developing MESD, a metric that measures explanation quality disparities across intersectional subgroups, and UEF, an optimization framework that balances utility, explanation fairness, and outcome fairness. Experimental results across multiple datasets show that UEF effectively balances these objectives and MESD captures explanation differences between groups.
Research about bias in machine learning has mostly focused on outcome-oriented fairness metrics (e.g., equalized odds) and on a single protected category. Although these approaches offer great insight into bias in ML, they provide limited insight into model procedure bias. To address this gap, we proposed multi-category explanation stability disparity (MESD), an intersectional, procedurally oriented metric that measures the disparity in the quality of explanations across intersectional subgroups in multiple protected categories. MESD serves as a complementary metric to outcome-oriented metrics, providing detailed insight into the procedure of a model. To further extend the scope of the holistic selection model, we also propose a multi-objective optimization framework, UEF (Utility-Explanation-Fairness), that jointly optimizes three objectives. Experimental results across multiple datasets show that UEF effectively balances objectives. Also, the results show that MESD can effectively capture the explanation difference between intersectional groups. This research addresses an important gap by examining explainability with respect to fairness across multiple protected categories.