Table of Contents
Preface
Section 1 – Conceptual Exposure
Chapter 1: Foundational Concepts of Explainability Techniques
Introduction to XAI
Understanding the key terms
Consequences of poor predictions
Summarizing the need for model explainability
Defining explanation methods and approaches
Dimensions of explainability
Addressing key questions of explainability
Understanding different types of explanation methods
Understanding the accuracy interpretability trade-off
Evaluating the quality of explainability methods
Criteria for good explainable ML systems
Auxiliary criteria of XAI for ML systems
Taxonomy of evaluation levels for explainable ML systems
Summary
References
Chapter 2: Model Explainability Methods
Technical requirements
Types of ...
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