#model-interpretability2 articles

2 articles

AINeutralarXiv – CS AI · 4h ago0

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Hierarchical Concept-based Interpretable Models

Researchers introduce Hierarchical Concept Embedding Models (HiCEMs), a new approach to make deep neural networks more interpretable by modeling relationships between concepts in hierarchical structures. The method includes Concept Splitting to automatically discover fine-grained sub-concepts without additional annotations, reducing the burden of manual labeling while improving model accuracy and interpretability.

AIBullisharXiv – CS AI · 4h ago0

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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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