While deep neural network models offer unmatched classification performance,
they are prone to learning spurious correlations in the data. Such dependencies
on confounding information can be difficult to detect using performance metrics
if the test data comes from the same distribution as the training data.
Interpretable ML methods such as post-hoc explanations or inherently
interpretable classifiers promise to identify faulty model reasoning. However,
there is mixed evidence whether many of these techniques are actually able to
do so. In this paper, we propose a rigorous evaluation strategy to assess an
explanation technique's ability to correctly identify spurious correlations.
Using this strategy, we evaluate five post-hoc explanation techniques and one
inherently interpretable method for their ability to detect three types of
artificially added confounders in a chest x-ray diagnosis task. We find that
the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net
provide the best performance and can be used to reliably identify faulty model
behavior.

深度神经网络模型容易学习到数据中的错误相关性，本文提出了一种评估方法，评估了五种事后解释技术和一种固有可解释的方法在胸部 X 射线诊断任务中对三种人为添加的混淆因素进行正确识别的能力，并发现 SHAP 和 Attri-Net 技术表现最佳，可用于可靠地识别错误的模型行为。