Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation

Leopoldo Bertossi; Jorge E. Leon

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arXiv · 2303.06516

Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation

Leopoldo Bertossi·Jorge E. Leon

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Abstract

The use of Shap scores has become widespread in Explainable AI. However, their computation is in general intractable, in particular when done with a black-box classifier, such as neural network. Recent research has unveiled classes of open-box Boolean Circuit classifiers for which Shap can be computed efficiently. We show how to transform binary neural networks into those circuits for efficient Shap computation.We use logic-based knowledge compilation techniques. The performance gain is huge, as we show in the light of our experiments.

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Leopoldo Bertossi, Jorge E. Leon. 2023-03-11. Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation. https://arxiv.org/abs/2303.06516

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