Editors:
- Assesses the current state of research on Explainable AI (XAI)
- Provides a snapshot of interpretable AI techniques
- Reflects the current discourse and provides directions of future development
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11700)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (22 chapters)
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Front Matter
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Part I Towards AI Transparency
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Front Matter
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Part II Methods for Interpreting AI Systems
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Front Matter
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Part III Explaining the Decisions of AI Systems
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Front Matter
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Part IV Evaluating Interpretability and Explanations
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Front Matter
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Part V Applications of Explainable AI
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Front Matter
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About this book
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner.
The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Reviews
“This is a very valuable collection for those working in any application of deep learning that looks for the key techniques in XAI at the moment. Readers from other areas in AI or new to XAI can get a glimpse of where cutting-edge research is heading.” (Jose Hernandez-Orallo, Computing Reviews, July 24, 2020)
Editors and Affiliations
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Fraunhofer Heinrich Hertz Institute, Berlin, Germany
Wojciech Samek
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Technische Universität Berlin, Berlin, Germany
Grégoire Montavon
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University of Oxford, Oxford, UK
Andrea Vedaldi
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Technical University of Denmark, Kgs. Lyngby, Denmark
Lars Kai Hansen
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Sekretariat MAR 4-1, Technical University of Berlin, Berlin, Germany
Klaus-Robert Müller
Bibliographic Information
Book Title: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Editors: Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-28954-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-28953-9Published: 30 August 2019
eBook ISBN: 978-3-030-28954-6Published: 10 September 2019
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XI, 439
Number of Illustrations: 33 b/w illustrations, 119 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Computing Milieux, Systems and Data Security, Computer Systems Organization and Communication Networks