Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

Table of Contents
About the Author
About the Technical Reviewers
Acknowledgments
Introduction
Chapter 1: Model Explainability and Interpretability
	Establishing the Framework
	Artificial Intelligence
		Need for XAI
		Explainability vs. Interpretability
		Explainability Types
		Tools for Model Explainability
		SHAP
		LIME
		ELI5
		Skater
		Skope_rules
		Methods of XAI for ML
		XAI Compatible Models
		XAI Meets Responsible AI
		Evaluation of XAI
	Conclusion
Chapter 2: AI Ethics, Biasness, and Reliability
	AI Ethics Primer
	Biasness in AI
	Data Bias
	Algorithmic Bias
	Bias Mitigation Process
	Interpretation Bias
	Training Bias
	Reliability in AI
	Conclusion
Chapter 3: Explainability for Linear Models
	Linear Models
	Linear Regression
	VIF and the Problems It Can Generate
		Final Model
		Model Explainability
	Trust in ML Model: SHAP
		Local Explanation and Individual Predictions in a ML Model
		Global Explanation and Overall Predictions in ML Model
	LIME Explanation and ML Model
	Skater Explanation and ML Model
	ELI5 Explanation and ML Model
	Logistic Regression
		Interpretation
		LIME Inference
	Conclusion
Chapter 4: Explainability for Non-Linear Models
	Non-Linear Models
	Decision Tree Explanation
	Data Preparation for the Decision Tree Model
		Creating the Model
	Decision Tree – SHAP
	Partial Dependency Plot
	PDP Using Scikit-Learn
	Non-Linear Model Explanation – LIME
	Non-Linear Explanation – Skope-Rules
	Conclusion
Chapter 5: Explainability for Ensemble Models
	Ensemble Models
		Types of Ensemble Models
	Why Ensemble Models?
	Using SHAP for Ensemble Models
	Using the Interpret Explaining Boosting Model
	Ensemble Classification Model: SHAP
	Using SHAP to Explain Categorical Boosting Models
	Using SHAP Multiclass Categorical Boosting Model
	Using SHAP for Light GBM Model Explanation
	Conclusion
Chapter 6: Explainability for Time Series Models
	Time Series Models
	Knowing Which Model Is Good
		Strategy for Forecasting
		Confidence Interval of Predictions
	What Happens to Trust?
	Time Series: LIME
	Conclusion
Chapter 7: Explainability for NLP
	Natural Language Processing Tasks
	Explainability for Text Classification
	Dataset for Text Classification
	Explaining Using ELI5
	Calculating the Feature Weights for Local Explanation
		Local Explanation Example 1
		Local Explanation Example 2
		Local Explanation Example 3
		Explanation After Stop Word Removal
	N-gram-Based Text Classification
	Multi-Class Label Text Classification Explainability
		Local Explanation Example 1
		Local Explanation Example 2
		Local Explanation Example 1
	Conclusion
Chapter 8: AI Model Fairness Using a What-If Scenario
	What Is the WIT?
	Installing the WIT
		Evaluation Metric
	Conclusion
Chapter 9: Explainability for Deep Learning Models
	Explaining DL Models
	Using SHAP with DL
		Using Deep SHAP
		Using Alibi
		SHAP Explainer for Deep Learning
		Another Example of Image Classification
		Using SHAP
		Deep Explainer for Tabular Data
	Conclusion
Chapter 10: Counterfactual Explanations for XAI Models
	What Are CFEs?
	Implementation of CFEs
	CFEs Using Alibi
	Counterfactual for Regression Tasks
	Conclusion
Chapter 11: Contrastive Explanations for Machine Learning
	What Is CE for ML?
	CEM Using Alibi
		Comparison of an Original Image vs. an Autoencoder-Generated Image
		CEM for Tabular Data Explanations
	Conclusion
Chapter 12: Model-Agnostic Explanations by Identifying Prediction Invariance
	What Is Model Agnostic?
	What Is an Anchor?
	Anchor Explanations Using Alibi
		Anchor Text for Text Classification
	Anchor Image for Image Classification
	Conclusion
Chapter 13: Model Explainability for Rule-Based Expert Systems
	What Is an Expert System?
		Backward and Forward Chaining
		Rule Extraction Using Scikit-Learn
			Need for a Rule-Based System
			Challenges of an Expert System
	Conclusion
Chapter 14: Model Explainability for Computer Vision
	Why Explainability for Image Data?
		Anchor Image Using Alibi
		Integrated Gradients Method
	Conclusion
Index