What AI says about SHAP
Since Jul 5Updated Nov 3, 2026Based on 32,159 AI responses
Reviewed by Dimitry Apollonsky
AI summary
SHAP is a library that explains the outputs of machine learning models using Shapley values.
Hosted on Read the Docs
Mentioned in
1%
of AI answers about SHAP and its rivals
- ChatGPT1%
- Google AI Mode1%
AI calls itwidely used,gold standard,industry standard,model-agnostic,open-source,cooperative game theory
Markets where AI mentions SHAP
VisibilityLowHighFew answers
All marketsChatGPTGoogle AI Mode
- Explainable AI & Model Monitoring PlatformsChatGPTGoogle AI Mode
- Enterprise AutoML Hyperparameter Tuning PlatformsOutside both engines’ top 25
- Fraud Detection and Prevention PlatformsOutside both engines’ top 25
- Predictive Churn Analytics SoftwareOutside both engines’ top 25
- Algorithmic Bias Auditing ServicesOutside both engines’ top 25
- Tech Career Bootcamps & CertificationsOutside both engines’ top 25
Topics where AI mentions SHAP
TopicMentioned in · on this topicRank
- AI picks mostMentioned in
- What is the best tool for explaining black-box model predictions to non-technical stakeholders?on this questionAI picks SHAP Documentation most100%
Excerpts where SHAP appeared in the AI's answer
Google AI ModeOct 12, 2026, Google AI Mode SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are the leading open-source tools.
Google AI ModeSep 25, 2026, Google AI Mode SHAP (SHapley Additive exPlanations) is widely considered the gold standard
- We are investigating why our model is confident but wrong. Who provides explainable AI (XAI) tools?on this questionAI picks Fiddler AI most100%
Excerpts where SHAP appeared in the AI's answer
Google AI ModeSep 23, 2026, Google AI Mode SHAP (Shapley Additive exPlanations): Widely considered the gold standard.
ChatGPTSep 23, 2026, ChatGPT SHAP — probably the most common starting point for feature attribution.
- I need a tool for explaining black-box model predictions that can generate easy-to-understand reports for stakeholders.on this questionAI picks InterpretML most88%
Excerpts where SHAP appeared in the AI's answer
Google AI ModeSep 24, 2026, Google AI Mode SHAP (SHapley Additive exPlanations) : Based on cooperative game theory, it allocates credit to each feature for the model's prediction.
ChatGPTSep 24, 2026, ChatGPT shap.readthedocs.io — the best-known open-source explanation engine if you're comfortable building the reporting layer yourself.
- 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 most54%
Excerpts where SHAP appeared in the AI's answer
Google AI ModeSep 20, 2026, Google AI Mode SHAP (Shapley Additive exPlanations) remains the mathematical gold standard for model explainability.
ChatGPTSep 12, 2026, ChatGPT I'd choose Fiddler over a pure explainability library such as SHAP because it combines XAI + fairness + monitoring + governance in one platform.
SHAP in AI answers
1 of 8
I need a tool for explaining black-box model predictions that can generate easy-to-understand reports for stakeholders.
ChatGPTChatGPT· Sep 24, 2026
shap.readthedocs.io — the best-known open-source explanation engine if you're comfortable building the reporting layer yourself.
Google AI ModeGoogle AI Mode· Sep 24, 2026
SHAP (SHapley Additive exPlanations) : Based on cooperative game theory, it allocates credit to each feature for the model's prediction.
Sources AI cites
- Your site15%
- Welcome to the SHAP documentation — SHAP latest documentation (opens in a new tab) · 5%
- Welcome to the SHAP documentation — SHAP latest documentation (opens in a new tab) · 3%
- shap.Explainer — SHAP latest documentation (opens in a new tab) · 3%
- API Reference — SHAP latest documentation (opens in a new tab) · 1%
- Welcome to the SHAP documentation — SHAP latest documentation (opens in a new tab) · 1%
- API Reference — SHAP latest documentation (opens in a new tab) · 1%
- shap.plots.waterfall — SHAP latest documentation (opens in a new tab) · 1%
- Welcome to the SHAP documentation — SHAP latest documentation (opens in a new tab) · 1%
- waterfall plot — SHAP latest documentation (opens in a new tab) · 1%
- Welcome to the SHAP documentation — SHAP latest documentation (opens in a new tab) · <1%
- 9%
- SHAP vs LIME Explained: Which XAI Tool Should You Use? (opens in a new tab) · 1%
- "Beyond the Black Box: Interpreting ML Models with SHAP" - Avik Basu (opens in a new tab) · 1%
- The 6 Benefits of Explainable AI (XAI) (opens in a new tab) · 1%
- Demystifying the Black Box & Building Trust in AI! (opens in a new tab) · 1%
- Demystifying the Black Box & Building Trust in AI! (opens in a new tab) · 1%
- Beyond the Black Box: Interpreting ML Models with SHAP (opens in a new tab) · 1%
- Decode Black Boxes with Explainable AI: Building Transparent AI Agents - YouTube (opens in a new tab) · <1%
- Methods for Explaining the Blackbox of Machine Learning ... (opens in a new tab) · <1%
- From Black Box to Glass Box: How Explainable AI Builds ... (opens in a new tab) · <1%
- Unified Approach to Interpret Machine Learning Model SHAP ... (opens in a new tab) · <1%
- 8%
- 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) · 2%
- An explainable data-driven optimization framework for industrial predictive maintenance scheduling (opens in a new tab) · 1%
- Improving predictive maintenance: Evaluating the impact of ... (opens in a new tab) · 1%
- An explainable artificial intelligence model for predictive ... (opens in a new tab) · 1%
- A Study on the Explainability of Thyroid Cancer Prediction: SHAP Values and Association-Rule Based Feature Integration Framework (opens in a new tab) · <1%
- Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types (opens in a new tab) · <1%
- When explainability turns into a threat - using xAI to fool a fake news ... (opens in a new tab) · <1%
- Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence (opens in a new tab) · <1%
- Explaining the black-box model: A survey of local interpretation ... (opens in a new tab) · <1%
- Uncertainty explanation of artificial intelligence models by SHAP (opens in a new tab) · <1%
- 7%
- Summary of “A Unified Approach to Interpreting Model Predictions” | by Tanisha | Medium (opens in a new tab) · 1%
- Explaining Your ML Models with SHAP: A Beginner’s Walkthrough to Truly Understand Predictions (opens in a new tab) · 1%
- Medium (opens in a new tab) · 1%
- AI Interview Mastery Series Day 4 — Trust by Design: Governance, Ethics & Compliance for C‑Level… (opens in a new tab) · <1%
- SHAP Happens: Unlocking the Secrets of Model Interpretability (opens in a new tab) · <1%
- Making AI Explainable to Non-Technical Stakeholders | by Annie Cushing | Medium (opens in a new tab) · <1%
- Demystifying the Black Box: A Beginner’s Guide to Explainable AI | by BIJAY CHOWDHURY | Medium (opens in a new tab) · <1%
- Telecom Customer Churn Prediction with XGBoost and SHAP (opens in a new tab) · <1%
- Explaining SHAP to my Business colleagues | by Guille Cornejo (opens in a new tab) · <1%
- The “Black Box” Problem in Healthcare AI: Building Trust and Transparency (opens in a new tab) · <1%
- 6%
- [2305.02012] A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME (opens in a new tab) · 1%
- Trust Oriented Explainable AI for Fake News Detection (opens in a new tab) · 1%
- [1705.07874] A Unified Approach to Interpreting Model Predictions (opens in a new tab) · 1%
- [1802.01933] A Survey Of Methods For Explaining Black Box Models (opens in a new tab) · 1%
- An Evaluation of Explanation Methods for Black-Box Detectors ... (opens in a new tab) · 1%
- Explainability in Practice: A Survey of Explainable NLP Across Various Domains (opens in a new tab) · 1%
- Explanations of Black-Box Model Predictions by Contextual Importance and Utility (opens in a new tab) · <1%
- [2401.07871] Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities (opens in a new tab) · <1%
- [2403.10415] Gradient based Feature Attribution in Explainable AI: A Technical Review (opens in a new tab) · <1%
- Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations (opens in a new tab) · <1%
- 4%
- Demystifying Black Box Models: The Importance of Explainable AI (opens in a new tab) · 2%
- Enhancing black-box models: Advances in explainable artificial ... (opens in a new tab) · 1%
- CRISP-ML (Q) mixed with an XAI algorithm to optimize decision-making... (opens in a new tab) · <1%
- Local explanation embeddings support both supervised clustering and... | Download Scientific Diagram (opens in a new tab) · <1%
- (PDF) Explainable Predictive Maintenance is Not Enough (opens in a new tab) · <1%
- (PDF) Explainable AI (XAI) for trustworthy and transparent decision-making: A theoretical framework for AI interpretability (opens in a new tab) · <1%
- Explainable AI Models for ERP-Based Predictive Maintenance and ... (opens in a new tab) · <1%
- 3%
- An explainable data-driven optimization framework for industrial predictive maintenance scheduling (opens in a new tab) · 1%
- Explainable AI (XAI): Core Ideas, Techniques, and Solutions | ACM Computing Surveys (opens in a new tab) · 1%
- A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME - Salih - 2025 - Advanced Intelligent Systems - Wiley Online Library (opens in a new tab) · <1%
- A critical review of state-of-the-art explainable artificial intelligence (XAI) methods and their business applications | Artificial Intelligence Review | Springer Nature Link (opens in a new tab) · <1%
- Explaining a series of models by propagating Shapley values | Nature Communications (opens in a new tab) · <1%
- Temporally rigorous and traceable predictive maintenance via joint labeler-model optimization | Scientific Reports (opens in a new tab) · <1%
- IOPscience::.. Error! (opens in a new tab) · <1%
- Explanations of Model Predictions with live and breakDown Packages (opens in a new tab) · <1%
- 2%
- Explaining a series of models by propagating Shapley values | Nature Communications (opens in a new tab) · 1%
- Algorithms to estimate Shapley value feature attributions | Nature Machine Intelligence (opens in a new tab) · 1%
- Interpretable ensemble remaining useful life prediction enables dynamic maintenance scheduling for aircraft engines | Scientific Reports (opens in a new tab) · <1%
- Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model | Scientific Reports (opens in a new tab) · <1%
- Survival-analysis-guided machine learning for failure and remaining-useful-life prediction in railway compressor | Scientific Reports (opens in a new tab) · <1%
- From local explanations to global understanding with explainable AI ... (opens in a new tab) · <1%
