VeriX: Towards Verified Explainability of Deep Neural Networks
Min Wu ⋅ Haoze Wu ⋅ Clark Barrett
2023 Poster
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.
Video
Chat is not available.
Successful Page Load