Book Content
chapters • 15h8m total length
1. Explaining Artificial Intelligence with Python
2. White Box XAI for AI Bias and Ethics
3. Explaining Machine Learning with Facets
4. Microsoft Azure Machine Learning Model Interpretability with SHAP
5. Building an Explainable AI Solution from Scratch
6. AI Fairness with Google's What-If Tool (WIT)
7. A Python Client for Explainable AI Chatbots
8. Local Interpretable Model-Agnostic Explanations (LIME)
9. The Counterfactual Explanations Method
10. Contrastive XAI
11. Anchors XAI
12. Cognitive XAI
