Kamath U., Liu J. Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Springer, 2021. — 328 p. — ISBN: 978-3-030-83355-8.This book is written both for readers entering the field and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students.Introduction to Interpretability and Explainability
Pre-model Interpretability and Explainability
Model Visualization Techniques and Traditional Interpretable Algorithms
Model Interpretability: Advances in Interpretable Machine Learning
Post-Hoc Interpretability and Explanations
Explainable Deep Learning
Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision
XAI: Challenges and Future