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arXiv · 2212.01051

VeriX: Towards Verified Explainability of Deep Neural Networks

Min Wu·Haoze Wu·Clark Barrett
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Abstract

We present VeriX (Verified eXplainability), a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models. We build such explanations and counterfactuals iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking. We evaluate our method on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.

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Min Wu, Haoze Wu, Clark Barrett. 2022-12-02. VeriX: Towards Verified Explainability of Deep Neural Networks. https://arxiv.org/abs/2212.01051

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