Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Abstract

This book takes an in-depth approach to presenting the fundamentals of explain-able AI through mathematical theory and practical use cases. The content is split into four parts: pre-model methods, intrinsic methods, post-hoc methods, and deep- learning methods. The first part introduces pre-model techniques for Explainable AI (XAI). Part Two presents classical and modern intrinsic model interpretability methods, while Part Three details the collection of post-hoc methods. Part Four dives into methods tailored specifically for deep learning models.