"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations

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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “and other computer vision domains despite known problems [2, 53, 334].”
Mathematical optimization in classification and regression trees (2021, background): “…number of shortcomings to this approach around, for instance, the generation of data or the loss of accuracy, which have been addressed with enhancements (Botari et al. 2020; Visani et al. 2020; Zhang et al. 2019), or with alternative approaches (Lundberg et al. 2020, 2018; Lundberg and Lee 2017).”
Explainability fact sheets: a framework for systematic assessment of explainable approaches (2019, background): “LIME [44] has recently been subjected to studies aiming to validate its usability [31, 50, 57], which discovered lack of stability for its explanations (S3) and shortcomings of their locality (U1), hence raising questions about the validation”
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020, background): “[237] illustrated the incidence of three sources of uncertainty, viz.”