Paper ID: 2212.01051

VeriX: Towards Verified Explainability of Deep Neural Networks

Min Wu, Haoze Wu, Clark Barrett

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.

Submitted: Dec 2, 2022