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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)
Conference series link(s): xxAI: International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers
Conference proceedings info: xxAI 2020.
Table of contents (18 chapters)
New Developments in Explainable AI
An Interdisciplinary Approach to Explainable AI
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.
- Computer Science
- Conference Proceedings
- Open Access
Editors and Affiliations
University of Natural Resources and Life Sciences Vienna, Vienna, Austria
University of Alberta, Edmonton, Canada
Princeton University, Princeton, USA
Seoul National University, Seoul, Korea (Republic of)
Technische Universität Berlin, Berlin, Germany
Fraunhofer Heinrich Hertz Institute, Berlin, Germany
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
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022
License: CC BY
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