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.
IEEE Transactions on Neural Networks , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
The Time-Geometric model combines GNNs for geometric patterns with temporal models and reports statistically significant accuracy gains in financial time series forecasting.
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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TabEmb: Joint Semantic-Structure Embedding for Table Annotation
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
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The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem
The Time-Geometric model combines GNNs for geometric patterns with temporal models and reports statistically significant accuracy gains in financial time series forecasting.