Outcome-fair credit models often exhibit hidden procedural bias through inconsistent reasoning across groups, which the CEC framework mitigates by enforcing consistent feature attributions via counterfactuals.
The intersectionality problem for algorithmic fairness,
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
Outcome-fair credit models often exhibit hidden procedural bias through inconsistent reasoning across groups, which the CEC framework mitigates by enforcing consistent feature attributions via counterfactuals.
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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.