Overview
- This book is open access, which means that you have free and unlimited access.
- Explainable AI (xAI) aims to create tools and models that are predictive, interpretable, understandable for humans
- Topic receiving huge interest in the machine learning and AI research communities
- Contributions in this volume are from leading researchers in the field, drawn from both academia and industry
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13200)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
Included in the following conference series:
Conference proceedings info: xxAI 2020.
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Table of contents (18 chapters)
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Front Matter
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Editorial
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Front Matter
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New Developments in Explainable AI
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Front Matter
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An Interdisciplinary Approach to Explainable AI
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Front Matter
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Keywords
About this book
Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans.
Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed.
After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
Editors and Affiliations
Bibliographic Information
Book Title: xxAI - Beyond Explainable AI
Book Subtitle: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
Editors: Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-031-04083-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022
Softcover ISBN: 978-3-031-04082-5Published: 17 April 2022
eBook ISBN: 978-3-031-04083-2Published: 16 April 2022
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 397
Number of Illustrations: 10 b/w illustrations, 114 illustrations in colour
Topics: Artificial Intelligence, Machine Learning