Explainable AI, LIME & SHAP for Model Interpretability, Unlocking AI's Decision-Making

Dive into Explainable AI (XAI) and learn how to build trust in AI systems with LIME and SHAP for model interpretability. Understand the importance of transparency and fairness in AI-driven decisions.
What is DataCamp? Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.

Explainable AI: Adapting LIME for video model interpretability, by Joachim Vanneste

Entropy, Free Full-Text

How to Interpret Machine Learning Models with LIME and SHAP

Diagnostics, Free Full-Text

The AiEdge+: Explainable AI - LIME and SHAP

The AiEdge+: Explainable AI - LIME and SHAP

Comprehending AI model decisions with SHAP Explainers and feature influence plots

How to Make AI Models Transparent and Explainable

The Future of Explainable AI rests upon Synthetic Data - MOSTLY AI

Interpretability part 3: opening the black box with LIME and SHAP - KDnuggets

Infosys Knowledge Institute Advanced Trends In AI: The Infosys Way

Unlocking the Black Box: Harnessing Explainable AI in Telecommunications, by Buse Bilgin, turkcell

From local explanations to global understanding with explainable AI for trees

Explainable AI (XAI): Unlocking Transparency and Trust in Artificial Intelligence, by Lazy Sith

Conceptual diagram showing the different post-hoc explainability