Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

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 ofExplainable AI-XAI Group

Single resource addressing the theory and practice of interpretability and explainability techniques using case studies Covers exploratory data analysis, feature importance, interpretable algorithms, and model agnostic techniques Includes over 220 color figures, tables and charts, and Git repos with data and Google colab notebooks for each chapter

Autorentext
Uday Kamath has more than 20 years of experience architecting and building analytics-based commercial solutions. He currently works as the Chief Analytics Officer at Digital Reasoning, one of the leading companies in AI for NLP and Speech Recognition, heading the Applied Machine Learning research group. Most recently, Uday served as the Chief Data Scientist at BAE Systems Applied Intelligence, building machine learning products and solutions for the financial industry, focused on fraud, compliance, and cybersecurity. Uday has previously authored many books on machine learning such as Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified and Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures. Uday has published many academic papers in different machine learning journals and conferences. Uday has a Ph.D. in Big Data Machine Learning and was one of the first in generalized scaling of machine learning algorithms using evolutionary computing. John Liu spent the past 22 years managing quantitative research, portfolio management and data science teams. He is currently CEO of Intelluron Corporation, an emerging AI-as-a-service solution company. Most recently, John was head of data science and data strategy as VP at Digital Reasoning. Previously, he was CIO of Spartus Capital, a quantitative investment firm in New York. Prior to that, John held senior executive roles at Citigroup, where he oversaw the portfolio solutions group that advised institutional clients on quantitative investment and risk strategies; at the Indiana Public Employees pension, where he managed the $7B public equities portfolio; at Vanderbilt University, where he oversaw the $2B equity and alternative investment portfolios; and at BNP Paribas, where he managedthe US index options and MSCI delta-one trading desks. He is known for his expertise in reinforcement learning applied to investment management and has authored numerous papers and book chapters on topics including natural language processing, representation learning, systemic risk, asset allocation, and EM theory. In 2016, John was named Nashville's Data Scientist of the Year. He earned his B.S., M.S., and Ph.D. in electrical engineering from the University of Pennsylvania and is a CFA Charterholder. James (Jimmy) Whitaker manages Applied Research at Digital Reasoning. He currently leads deep learning developments in speech analytics in the FinTech space, and has spent the last 4 years building machine learning applications for NLP, Speech Recognition, and Computer Vision. He received his masters in Computer Science from the University of Oxford, where he received a distinction for his application of machine learning in the field of Steganalysis after completing his undergraduate degrees in Electrical Engineering and Computer Science from Christian Brothers University. Prior to his work in deep learning, Jimmy worked as a concept engineer and risk manager for complex transportation initiatives.

Klappentext

1. Introduction to Interpretability and Explainability.- 2. Pre-Model Interpretability and Explainability.- 3. Model Visualization Techniques and Traditional Interpretable Algorithms.- 4. Model Interpretability: Advances in Interpretable Machine Learning.- 5. Post-hoc Interpretability and Explanations.- 6. Explainable Deep Learning.- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision.- 8. XAI: Challenges and Future.

   Inhalt

   1. Introduction to Interpretability and Explainability.- 2. Pre-Model Interpretability and Explainability.- 3. Model Visualization Techniques and Traditional Interpretable Algorithms.- 4. Model Interpretability: Advances in Interpretable Machine Learning.- 5. Post-hoc Interpretability and Explanations.- 6. Explainable Deep Learning.- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision.- 8. XAI: Challenges and Future.