AIBullisharXiv – CS AI · Mar 24/107
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Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations
Researchers introduce COLA, a framework that refines counterfactual explanations in AI models by using optimal transport theory and Shapley values to achieve the same prediction changes with 26-45% fewer feature modifications. The method works across different datasets and models to create more actionable and clearer AI explanations.
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