How Sensitive are Sensitivity-Based Explanations?

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Explainable machine learning in deployment (2019, methodology): “, after selecting feature importance as the technique, an organization can compare LIME [50] and SHAP [34] explanations via the faithfulness criterion [69]).”
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning (2019, background): “Recent work ( 25,63,64 ) all conduct robustness evaluation with metrics on sensitivity, beyond the evaluation on predictability, ﬁdelity or persuasibility.”