论文标题
Verix：旨在验证深度神经网络的解释性
VeriX: Towards Verified Explainability of Deep Neural Networks
论文作者
论文摘要
我们提出Verix（已验证的解释性），该系统可在机器学习模型的决策范围内产生最佳的强大解释并产生反事实。我们使用约束求解技术和基于功能级敏感性排名的启发式方法在迭代上构建了此类解释和反事实。我们在图像识别基准和自动驾驶飞机滑行的现实情况下评估我们的方法。
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.
