We study the robustness of global post-hoc explanations for predictive models
trained on tabular data. Effects of predictor features in black-box supervised
learning are an essential diagnostic tool for model debugging and scientific
discovery in applied sciences. However, how vulnerable they are to data and
model perturbations remains an open research question. We introduce several
theoretical bounds for evaluating the robustness of partial dependence plots
and accumulated local effects. Our experimental results with synthetic and
real-world datasets quantify the gap between the best and worst-case scenarios
of (mis)interpreting machine learning predictions globally.

我们研究了基于表格数据训练的预测模型的全局事后解释的健壮性。预测模型中预测特征的影响是模型调试和应用科学中的重要诊断工具，然而它们在面对数据和模型扰动时的脆弱性仍然是一个未解决的研究问题。我们引入了一些理论界限来评估偏依赖图和累积局部效应的健壮性。我们的实验结果使用合成和真实数据集量化了（误）解释机器学习预测全局情况下最佳和最差情况之间的差距。