mlsanityData to 14 Oct 2026

Regularizing Black-box Models for Improved Interpretability

arXiv 1902.06787 ↗First posted 18 Feb 2019cs.LG stat.MLcited by 35

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Abstract (arXiv)

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regularizes a model for explanation quality at training time. Importantly, these regularizers are differentiable, model agnostic, and require no domain knowledge to define. We demonstrate that post-hoc explanations for ExpO-regularized models have better explanation quality, as measured by the common fidelity and stability metrics. We verify that improving these metrics leads to significantly more useful explanations with a user study on a realistic task.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Feb 2019.

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Citing papers per year: 2021: 11; 2022: 10; 2023: 6; 2024: 4; 2025: 3; 2026: 1.

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  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 302
  2. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  3. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
  4. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 85
  5. 2021A Survey on Interpretable Reinforcement Learningcited by 73
  6. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 53
  7. 2022Concept-level Debugging of Part-Prototype Networkscited by 52
  8. 2021Shapley Explanation Networkscited by 41
  9. 2021DISSECT: Disentangled Simultaneous Explanations via Concept Traversalscited by 37
  10. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 36
  11. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 30
  12. 2021Refining Language Models with Compositional Explanationscited by 27
23 more
  1. 2021The Role of Explainability in Assuring Safety of Machine Learning in Healthcarecited by 26
  2. 2021What to Learn, and How: Toward Effective Learning from Rationalescited by 25
  3. 2023Self-Interpretable Time Series Prediction with Counterfactual Explanationscited by 24
  4. 2022Use-Case-Grounded Simulations for Explanation Evaluationcited by 19
  5. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  6. 2023FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysiscited by 12
  7. 2021Learning to Predict with Supporting Evidence: Applications to Clinical Risk Predictioncited by 7
  8. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
  9. 2021Defense Against Explanation Manipulationcited by 6
  10. 2024Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationalescited by 5
  11. 2023Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencodercited by 3
  12. 2024Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data setcited by 3
  13. 2022Responsibility: An Example-based Explainable AI approach via Training Process Inspectioncited by 2
  14. 2025Joint Explainability-Performance Optimization With Surrogate Models for AI-Driven Edge Servicescited by 2
  15. 2021Training Deep Models to be Explained with Fewer Examplescited by 1
  16. 2023Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptationcited by 1
  17. 2024Explanation Regularisation through the Lens of Attributionscited by 1
  18. 2021Harnessing value from data science in business: ensuring explainability and fairness of solutionscited by 0
  19. 2023Learning Locally Interpretable Rule Ensemblecited by 0
  20. 2023Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizerscited by 0
  21. 2024Improving Decision Sparsitycited by 0
  22. 2025Explainability and Continual Learning meet Federated Learning at the Network Edgecited by 0
  23. 2026Spatial Attention Noise Masking for Causally Sufficient Interpretabilitycited by 0