MESD: Detecting and Mitigating Procedural Bias in Intersectional Groups
arXiv:2603.13452v1 Announce Type: new Abstract: 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. Al
arXiv:2603.13452v1 Announce Type: new Abstract: 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.
Executive Summary
The article introduces MESD, a novel intersectional metric designed to detect procedural bias in machine learning models by measuring disparities in explanation quality across multiple protected categories. Complementing existing outcome-oriented fairness metrics, MESD offers a procedural lens that enhances understanding of model behavior for intersectional subgroups. Alongside MESD, the authors propose UEF, a multi-objective optimization framework integrating utility, explanation, and fairness. Experimental validation across datasets demonstrates the effectiveness of both MESD and UEF in capturing procedural disparities and balancing multiple objectives. This work fills a critical gap by addressing explainability through an intersectional and procedural lens, advancing fairness in ML beyond traditional single-category or outcome-centric approaches.
Key Points
- ▸ Introduction of MESD as an intersectional procedural bias metric
- ▸ Proposal of UEF as a multi-objective optimization framework aligning utility, explanation, and fairness
- ▸ Empirical validation showing effectiveness in capturing intersectional procedural disparities
Merits
Innovative Metric
MESD introduces a unique procedural-oriented lens for intersectional fairness, complementing existing outcome-based metrics.
Holistic Framework
UEF provides a structured approach to jointly optimize multiple fairness-related objectives, enhancing model design.
Demerits
Complexity
The intersectional and multi-objective nature of MESD and UEF may increase computational overhead and implementation complexity.
Limited Scope
Current validation is based on existing datasets; broader applicability across diverse real-world contexts remains to be validated.
Expert Commentary
This paper represents a significant step forward in the evolution of fairness-aware machine learning. By introducing MESD, the authors bridge a longstanding disconnect between procedural and outcome-based fairness metrics, particularly for intersectional subgroups that have historically been inadequately addressed. The conceptualization of procedural bias through explanation stability disparity is both novel and methodologically sound, offering a richer diagnostic tool for evaluators. Moreover, the integration of UEF into a multi-objective framework reflects a sophisticated understanding of the trade-offs inherent in fairness engineering. While the empirical evidence is compelling, the authors should consider extending their validation to include longitudinal or domain-specific datasets to strengthen generalizability. Overall, this work deserves attention as a foundational contribution to equitable AI development.
Recommendations
- ✓ Adopt MESD as a standard supplementary metric in fairness audits for intersectional models.
- ✓ Integrate UEF into academic and industry fairness evaluation frameworks as a baseline multi-objective optimization tool.