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.
What will it take to generate fairness-preserving explanations?
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
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.
A conditional invariance framework defines explanation fairness as explanations being statistically independent of protected attributes given task-relevant features, unifying existing metrics and enabling procedural bias audits.
citing papers explorer
-
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.
-
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.
-
Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI
A conditional invariance framework defines explanation fairness as explanations being statistically independent of protected attributes given task-relevant features, unifying existing metrics and enabling procedural bias audits.