mlsanityData to 14 Oct 2026

InterpretML: A Unified Framework for Machine Learning Interpretability

arXiv 1909.09223 ↗First posted 19 Sep 2019cs.LG stat.MLcited by 246

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Abstract (arXiv)

InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability - glassbox models, which are machine learning models designed for interpretability (ex: linear models, rule lists, generalized additive models), and blackbox explainability techniques for explaining existing systems (ex: Partial Dependence, LIME). The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform. InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models. The MIT licensed source code can be downloaded from github.com/microsoft/interpret.

Key facts. Cited by 246 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Sep 2019.

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Citing papers per year: 2021: 41; 2022: 39; 2023: 43; 2024: 43; 2025: 32; 2026: 48.

246 papers.

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  1. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
  2. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  3. 2021Trustworthy AI: From Principles to Practicescited by 207
  4. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  5. 2021Robustness Gym: Unifying the NLP Evaluation Landscapecited by 108
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 91
  8. 2021NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learningcited by 88
  9. 2022From Shapley Values to Generalized Additive Models and backcited by 60
  10. 2022Neural Basis Models for Interpretabilitycited by 56
  11. 2021Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAycited by 55
  12. 2022VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformerscited by 53
38 more
  1. 2022A Survey on Explainable Anomaly Detectioncited by 39
  2. 2024PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI Systemcited by 31
  3. 2023Exploring and Interacting with the Set of Good Sparse Generalized Additive Modelscited by 30
  4. 2022Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selectioncited by 28
  5. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  6. 2021Choice modelling in the age of machine learning -- discussion papercited by 26
  7. 2025Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistantcited by 26
  8. 2022Scalable Interpretability via Polynomialscited by 25
  9. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  10. 2024Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigationcited by 25
  11. 2021Accuracy, Interpretability, and Differential Privacy via Explainable Boostingcited by 24
  12. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  13. 2021Coalesced Multi-Output Tsetlin Machines with Clause Sharingcited by 22
  14. 2022OmniXAI: A Library for Explainable AIcited by 21
  15. 2022Fast Sparse Classification for Generalized Linear and Additive Modelscited by 19
  16. 2022Use-Case-Grounded Simulations for Explanation Evaluationcited by 19
  17. 2023Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Lifecited by 19
  18. 2021GAM Changer: Editing Generalized Additive Models with Interactive Visualizationcited by 18
  19. 2023Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Meancited by 18
  20. 2021SurvNAM: The machine learning survival model explanationcited by 16
  21. 2021Fooling Partial Dependence via Data Poisoningcited by 16
  22. 2023Improving Neural Additive Models with Bayesian Principlescited by 15
  23. 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
  24. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  25. 2021Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Healthcited by 13
  26. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  27. 2021Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Setcited by 12
  28. 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systemscited by 12
  29. 2024Gaussian Process Neural Additive Modelscited by 11
  30. 2023A Comprehensive Review on Financial Explainable AIcited by 10
  31. 2024Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunitiescited by 10
  32. 2021ControlBurn: Feature Selection by Sparse Forestscited by 9
  33. 2023GAM Coach: Towards Interactive and User-centered Algorithmic Recoursecited by 9
  34. 2024Intrinsic User-Centric Interpretability through Global Mixture of Expertscited by 9
  35. 2021Ensembles of Random SHAPscited by 8
  36. 2025Rethinking LLM Evaluation: Can We Evaluate LLMs with 200x Less Data?cited by 8
  37. 2021Translational NLP: A New Paradigm and General Principles for Natural Language Processing Researchcited by 7
  38. 2021Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machinecited by 7

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