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 of Explainable AI-XAI Group

這本書既適合初入此領域的讀者，也適合具備人工智慧背景並對開發實際應用有興趣的從業人員。該書對於業界和學術界的從業人員和研究人員來說是一個很好的資源，其中討論的案例研究和相關材料可以作為各種項目和課堂實作的靈感來源。我一定會將這本書作為我教授的課程的個人資源，並強烈推薦給我的學生。
--Dr. Carlotta Domeniconi, 雷根大學計算機科學系副教授

這本書提供了一個介紹機器學習可解釋性的課程，涵蓋了每個階段。作者提供了引人入勝的例子，證明了像引導解釋性討論這樣的核心教學實踐可以被教師教授和學習，並需要持續努力。這是增強人工智慧和機器學習成果質量的更好方式。我希望這本書能成為教師、數據科學教育者和機器學習開發人員的入門指南，讓我們一起實踐解釋性機器學習的藝術。
--Anusha Dandapani, 聯合國國際電信聯盟首席數據與分析官，紐約大學兼職教師

這是一本很棒的書！我很高興下一代科學家終於能夠學習這個重要的主題。這是我見過的第一本具有最新且全面的內容的書。感謝作者！
--Dr. Cynthia Rudin, 雷根大學計算機科學、電機與計算機工程、統計科學、生物統計與生物信息學教授

迄今為止，關於可解釋人工智慧的文獻相對稀缺，主要涉及SHAP和LIME等主流算法。這本書通過對科學界在過去5-10年中提出的各種算法進行廣泛回顧，填補了這一空白。對於新手或已熟悉該領域並希望擴展知識的人來說，這本書是一個很好的指南。從可視化、可解釋方法、局部和全局解釋、時間序列方法到深度學習的全面回顧，提供了一個無與倫比的資訊來源，目前在其他地方無法找到。此外，具有生動例子的筆記本是一個很好的補充，使這本書對於任何級別的從業人員更具吸引力。
總的來說，作者在不失實踐方面的前提下，為讀者提供了廣泛的涵蓋範圍，使這本書真正獨特，是任何數據科學家圖書館的重要補充。
Dr. Andrey Sharapov, 產品數據科學家，可解釋人工智慧專家和演講者，可解釋人工智慧-XAI Group創始人

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.

Uday Kamath在統計學、優化、機器學習、自然語言處理和語音識別以及可解釋的人工智慧領域中，花費了超過二十年的時間開發分析產品。Uday擁有可擴展機器學習的博士學位，並在人工智慧領域的許多期刊、會議和書籍中做出了貢獻。他是《Deep Learning for NLP and Speech Recognition》、《Mastering Java Machine Learning》和《Machine Learning: End-to-End Guide for Java Developers》等書籍的作者。他曾擔任Digital Reasoning的首席分析官、Falkonry的顧問以及BAE Systems Applied Intelligence的首席數據科學家等多個高級職位。他利用人工智慧在監控、合規、網絡安全、金融犯罪、反洗錢和保險詐騙等領域建立了產品和解決方案。目前，Uday在Smarsh擔任首席分析官，負責數據科學、深度學習和可解釋的人工智慧在金融和醫療領域中的研究以及語音和文本的現代技術。

John Chih Liu博士是Intelluron Corporation的首席執行官。他曾擔任數量研究、投資組合管理和數據科學組織的高級執行職位，包括Digital Reasoning Systems的應用機器學習數據科學副總裁、Vanderbilt University基金的股權策略董事總經理以及BNP Paribas的指數期權交易主管。他經常在自然語言處理、強化學習、資產配置、系統風險和EM理論等主題上發表演講和著作。John於2016年被評為納什維爾的年度數據科學家，2018年入圍年度社區領袖，2020年入圍年度創新者。他在賓夕法尼亞大學獲得了學士、碩士和博士學位，並擁有CFA資格證書，是全球數據科學社區的倡導者，也是國際科學和工程博覽會的支持者。