Explanations for machine learning models can be hard to interpret or be wrong. Combining an explanation method with an uncertainty estimation method produces explanation uncertainty. Evaluating explanation uncertainty is difficult. In this paper we propose sanity checks for uncertainty explanation methods, where a weight and data randomization tests are defined for explanations with uncertainty, allowing for quick tests to combinations of uncertainty and explanation methods. We experimentally show the validity and effectiveness of these tests on the CIFAR10 and California Housing datasets, noting that Ensembles seem to consistently pass both tests with Guided Backpropagation, Integrated Gradients, and LIME explanations.

中文翻译：

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解释不确定性的健全性检查

对机器学习模型的解释可能难以解释或错误。将解释方法与不确定性估计方法相结合会产生解释不确定性。评估解释的不确定性是很困难的。在本文中，我们提出了对不确定性解释方法的健全性检查，其中为不确定性的解释定义了权重和数据随机化测试，从而可以对不确定性和解释方法的组合进行快速测试。我们通过实验证明了这些测试在 CIFAR10 和加州住房数据集上的有效性和有效性，并注意到集成似乎始终通过引导反向传播、积分梯度和 LIME 解释的这两项测试。