MESD quantifies disparities in explanation quality across intersectional subgroups by combining label-aware aggregation, empirical-Bayes shrinkage, and CVaR weighting within a multi-objective optimization framework.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.
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MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups
MESD quantifies disparities in explanation quality across intersectional subgroups by combining label-aware aggregation, empirical-Bayes shrinkage, and CVaR weighting within a multi-objective optimization framework.
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GESD: Beyond Outcome-Oriented Fairness
The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.