  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.
