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.
--Dr. Carlotta Domeniconi, Associate Professor, Computer Science
Department, GMU
This book offers a curriculum for introducing interpretability to
machine learning at every stage. The authors provide compelling examples
that a core teaching practice like leading interpretive discussions can
be taught and learned by teachers and sustained effort. And what better
way to strengthen the quality of AI and Machine learning outcomes. I
hope that this book will become a primer for teachers, data Science
educators, and ML developers, and together we practice the art of
interpretive machine learning.
--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct
Faculty, NYU
This is a wonderful book! I'm pleased that the next generation of
scientists will finally be able to learn this important topic. This is
the first book I've seen that has up-to-date and well-rounded coverage.
Thank you to the authors!
--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and
Computer Engineering, Statistical Science, and Biostatistics &
Bioinformatics
Literature on Explainable AI has up until now been relatively scarce and
featured mainly mainstream algorithms like SHAP and LIME. This book has
closed this gap by providing an extremely broad review of various
algorithms proposed in the scientific circles over the previous 5-10
years. This book is a great guide to anyone who is new to the field of
XAI or is already familiar with the field and is willing to expand their
knowledge. A comprehensive review of the state-of-the-art Explainable AI
methods starting from visualization, interpretable methods, local and
global explanations, time series methods, and finishing with deep
learning provides an unparalleled source of information currently
unavailable anywhere else. Additionally, notebooks with vivid examples
are a great supplement that makes the book even more attractive for
practitioners of any level.
Overall, the authors provide readers with an enormous breadth of
coverage without losing sight of practical aspects, which makes this
book truly unique and a great addition to the library of any data
scientist.
Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and
Speaker, Founder of Explainable AI-XAI Group
