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

• URL: http://arxiv.org/abs/2303.06516v3
• Date: Sun, 23 Jul 2023 01:21:47 GMT
• Title: Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation
• Abstract summary: We show how to transform binary neural networks into those circuits for efficient Shap computation. The performance gain is huge, as we show in the light of our experiments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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