Poster
VeriX: Towards Verified Explainability of Deep Neural Networks
Min Wu · Haoze Wu · Clark Barrett
Great Hall & Hall B1+B2 (level 1) #1511
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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