Explainable Artificial Intelligence: An Introduction to XAI - Rilegato

Kamath, Uday; Liu, John

Rilegato

ISBN 10: 3030833550 ISBN 13: 9783030833558

Casa editrice: Springer Nature Switzerland AG, 2021

Riassunto

This book takes an in-depth approach to presenting the fundamentals of explainable AI through mathematical theory and practical use cases. The content is split into five parts: 1) pre-hoc techniques involving exploratory data analysis, visualization and feature engineering, 2) intrinsic and interpretable machine learning, 3) model-agnostic methods, 4) explainable deep learning methods and 5) A survey of interpretable and explainable methods applied to time series, natural language processing and computer vision.

The field of Explainable AI addresses one of the most significant shortcomings of machine learning and deep learning algorithms today: the interpretability of models. As algorithms become more powerful and make predictions with better accuracy, it becomes increasingly important to understand how and why a prediction is made. Without interpretability and explainability, it would be difficult for the users to trust the predictions of real-life applications of AI.

Explainable Artificial Intelligence: AN Introduction to XAI offers its readers a collection of techniques and case studies that serves as an accessible introduction for those entering the field, and for current AI/ML researchers as they integrate explainability into their research and innovation.

Su questo libro

Informazioni sull?autore

Uday Kamath has spent more than two decades developing analytics products in statistics, optimization, machine learning, NLP and speech recognition, and explainable AI. Uday has a Ph.D. in scalable machine learning and has contributed to many journals, conferences, and books in the field of AI. He is the author of books such as Deep Learning for NLP and Speech Recognition, Mastering Java Machine Learning, and Machine Learning: End-to-End Guide for Java Developers. He held many senior roles: Chief Analytics Officer for Digital Reasoning, Advisor for Falkonry, and Chief Data Scientist for BAE Systems Applied Intelligence. He has built products and solutions using AI in surveillance, compliance, cybersecurity, financial crime, anti-money laundering, and insurance fraud. Uday currently works as the Chief Analytics Officer for Smarsh. He is responsible for Data Science, research of analytics products employing deep learning and explainable AI, and modern techniques in speech and text used in the financial domain and healthcare.

John Chih Liu, PhD, CFA is Chief Executive Officer of Intelluron Corporation. Previously, he held senior executive roles overseeing quantitative research, portfolio management and data science organizations, including as VP of Data Science, Applied Machine Learning at Digital Reasoning Systems, MD of Equity Strategies at the Vanderbilt University endowment, and Head of Index Options Trading at BNP Paribas. He is a frequent speaker and published author on topics including natural language processing, reinforcement learning, asset allocation, systemic risk and EM theory. John was named Nashville's Data Scientist of the Year in 2016, Finalist for Community Leader of the Year in 2018, and Finalist for Innovator of the Year in 2020. He earned his B.S., M.S., and Ph.D. in electrical engineering from the University of Pennsylvania and is a CFA Charterholder, advocate for the global data science community and supporter of the International Science and Engineering Fair.

Dalla quarta di copertina

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

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

Dettagli dell?edizione

• Editore: Springer Nature Switzerland AG
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030833550
• ISBN 13: 9783030833558
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 392
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania