Author

Ming Lei

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#artificial intelligence Preprint Sep 2026

A unified framework for global and local interpretability using adaptive derivative-ordered random explanation

Experiments across three data modalities demonstrate that ADORE outperforms existing methods such as LIME and SHAP in handling complex interactions and computational efficiency, while providing detailed and reliable explanations.

Le-Men Chao, Ming Lei, An-Ran Fang · 0 citations

#artificial intelligence Preprint Sep 2026

Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details

This study proposes a formal definition of data storytelling in IML, introduces the DIST Pyramid to align data storytelling with IML, and presents the I-P-O Model to describe their interactions, and develops an architecture to explain AI decisions through distinct "What-if" and "Why-not" event-generation processes.

Le-Men Chao, Zi-Xuan Yang, An-Ran Fang et al. · 0 citations