arXiv Machine Learning
Revisiting Explainable AI through Model-Independent Concept Dictionaries
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Revisiting Explainable AI through Model-Independent Concept Dictionaries
Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transparency. However, existing XAI methods typically as...
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Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
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Beyond Explanation: Debugging Medical Imaging Models via Concept Intervention
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SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis
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A unified framework for global and local interpretability using adaptive derivative-ordered random explanation
The paper introduces Adaptive Derivative-Ordered Random Explanation (ADORE), a unified framework that uses first- and second-order derivatives to capture nonlinear feature interactions and feature-sample dynamics. ADORE combines global feature importance with local sample contributions, quantifying both magnitude and direction of feature impact while identifying critical samples. It achieves computational efficiency via randomized SVD and dynamic sparsity detection, outperforming LIME and SHAP across tabular, text, and image data, and is released as an open-source Python package on GitHub.