VeriX: Towards Verified Explainability of Deep Neural Networks

We present VeriX, a system for producing optimal robust explanations (La Malfa et al. 2021) for machine learning models. We build robust explanations iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking. We evaluate our approach on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.