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On the Robustness of Interpretability Methods

arXiv 1806.08049 ↗First posted 21 Jun 2018cs.LG stat.MLcited by 310

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Abstract (arXiv)

We argue that robustness of explanations---i.e., that similar inputs should give rise to similar explanations---is a key desideratum for interpretability. We introduce metrics to quantify robustness and demonstrate that current methods do not perform well according to these metrics. Finally, we propose ways that robustness can be enforced on existing interpretability approaches.

Key facts. Cited by 310 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2018.

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Citing papers per year: 2021: 50; 2022: 62; 2023: 50; 2024: 57; 2025: 44; 2026: 47.

310 papers.

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  1. 2022What the DAAM: Interpreting Stable Diffusion Using Cross Attentioncited by 233
  2. 2022The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspectivecited by 156
  3. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  4. 2022OpenXAI: Towards a Transparent Evaluation of Model Explanationscited by 125
  5. 2021Acquisition of Chess Knowledge in AlphaZerocited by 120
  6. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  7. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  8. 2023Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanationscited by 90
  9. 2022Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanationscited by 77
  10. 2021Explaining Time Series Predictions with Dynamic Maskscited by 72
  11. 2022TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversationscited by 65
  12. 2022A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributionscited by 64
38 more
  1. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  2. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  3. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  4. 2022Framework for Evaluating Faithfulness of Local Explanationscited by 55
  5. 2022Rethinking Explainability as a Dialogue: A Practitioner's Perspectivecited by 52
  6. 2022Rethinking Stability for Attribution-based Explanationscited by 52
  7. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  8. 2021GLocalX -- From Local to Global Explanations of Black Box AI Modelscited by 48
  9. 2021Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networkscited by 48
  10. 2021Towards the Unification and Robustness of Perturbation and Gradient Based Explanationscited by 47
  11. 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guaranteescited by 45
  12. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  13. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  14. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 38
  15. 2022A Comparative Study of Faithfulness Metrics for Model Interpretability Methodscited by 38
  16. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  17. 2022Can Rationalization Improve Robustness?cited by 36
  18. 2022WeightedSHAP: analyzing and improving Shapley based feature attributionscited by 36
  19. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  20. 2022Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysiscited by 34
  21. 2024LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivitycited by 32
  22. 20213DB: A Framework for Debugging Computer Vision Modelscited by 31
  23. 2021On the Sensitivity and Stability of Model Interpretations in NLPcited by 30
  24. 2021RKHS-SHAP: Shapley Values for Kernel Methodscited by 30
  25. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  26. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  27. 2022SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretabilitycited by 28
  28. 2021On Locality of Local Explanation Modelscited by 25
  29. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 25
  30. 2023An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learningcited by 25
  31. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  32. 2021Best of both worlds: local and global explanations with human-understandable conceptscited by 22
  33. 2021CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Modelscited by 20
  34. 2022Attribution-based Explanations that Provide Recourse Cannot be Robustcited by 20
  35. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  36. 2022Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?cited by 19
  37. 2021Logic Traps in Evaluating Attribution Scorescited by 18
  38. 2021Scrutinizing XAI using linear ground-truth data with suppressor variablescited by 18

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