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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TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
Constraining neural network partial dependence to match domain-knowledge functional forms during training improves predictive accuracy, data efficiency, and explanation faithfulness on regression problems.
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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A foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
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Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Constraining neural network partial dependence to match domain-knowledge functional forms during training improves predictive accuracy, data efficiency, and explanation faithfulness on regression problems.