What AI says about InterpretML
InterpretML is an open-source machine learning interpretability toolkit developed by Microsoft for explaining both glass-box and black-box models. It is regularly recommended for explaining black-box predictions to stakeholders and investigating model decisions through feature attribution.
AI calls itopen-source package,interactive visualizations,glass-box models,black-box explainers,glassbox models,comprehensive
Since Jul 5Updated Nov 5, 2026Based on 16,764 AI answers
Reviewed by Dimitry Apollonsky
1%
of AI answers about InterpretML and its rivals · since Jul 5
Based on 16,764 AI answers
- ChatGPT1%
- Google AI Mode1%
How often AI mentions InterpretML
of AI answers about InterpretML and its rivals
Markets where AI mentions InterpretML
Topics where AI mentions InterpretML
- AI picks mostMentioned in
- I need a tool for explaining black-box model predictions that can generate easy-to-understand reports for stakeholders.on this questionAI picks InterpretML most84%
Excerpts where InterpretML appeared in the AI's answer
Google AI ModeSep 24, 2026, Google AI Mode InterpretML : An open-source package from Microsoft that combines state-of-the-art machine learning interpretability techniques
ChatGPTSep 16, 2026, ChatGPT InterpretML — a good choice if you want an open-source Python framework and interactive explanations.
- What is the best tool for explaining black-box model predictions to non-technical stakeholders?on this questionAI picks SHAP Documentation most67%
Excerpts where InterpretML appeared in the AI's answer
Google AI ModeOct 12, 2026, Google AI Mode Alternative (InterpretML): If you want a unified dashboard that packages various glassbox and black-box explanation methods into business-ready visual interfaces, look into Microsoft's InterpretML.
ChatGPTSep 25, 2026, ChatGPT InterpretML — useful if you want an open-source package with interactive visualizations and both global and local explanations.
- We are investigating why our model is confident but wrong. Who provides explainable AI (XAI) tools?on this questionAI picks Fiddler AI most59%
Excerpts where InterpretML appeared in the AI's answer
ChatGPTSep 19, 2026, ChatGPT Microsoft InterpretML / Responsible AI tooling — Microsoft's ecosystem includes interpretable “glass-box” models and post-hoc explanation methods such as SHAP and LIME.
Google AI ModeSep 19, 2026, Google AI Mode Microsoft InterpretML: An open-source framework that unifies multiple state-of-the-art interpretability techniques
- The problem is our AI models are a "black box." What's the best AI model explainability and monitoring tool for ensuring fairness and transparency?on this questionAI picks Fiddler AI most7%
InterpretML in AI answers
What is the best tool for explaining black-box model predictions to non-technical stakeholders?
Sites AI cites
- 5%
- [2305.02012] A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME (opens in a new tab) · 2%
- Interpretable & Explorable Approximations of Black Box Models (opens in a new tab) · 1%
- Benchmarking and Survey of Explanation Methods for Black Box Models (opens in a new tab) · 1%
- Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction (opens in a new tab) · 1%
- Exploring the Impact of Explainable AI and Cognitive Capabilities on ... (opens in a new tab) · 1%
- 4%
- The rationality of explanation or human capacity? Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance - ScienceDirect (opens in a new tab) · 1%
- Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination (opens in a new tab) · 1%
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration (opens in a new tab) · 1%
- The role of eXplainable Artificial Intelligence (XAI) in smart grids (opens in a new tab) · 1%
- In AI we trust: How to build trustworthy artificial intelligence models (opens in a new tab) · 1%
The page AI cites most for InterpretML
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Mentioned in

