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Lecture Notes in Artificial Intelligence Lect.Notes ComputerState-of-the-Art Surveys

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Editors: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (Eds.)

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  • 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
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eBook 63,06 €
price for Spain (gross)
  • ISBN 978-3-030-28954-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 76,95 €
price for Spain (gross)
  • ISBN 978-3-030-28953-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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.

Table of contents (22 chapters)

Table of contents (22 chapters)

Buy this book

eBook 63,06 €
price for Spain (gross)
  • ISBN 978-3-030-28954-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 76,95 €
price for Spain (gross)
  • ISBN 978-3-030-28953-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

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 Artificial Intelligence
Series Volume
11700
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-28954-6
DOI
10.1007/978-3-030-28954-6
Softcover ISBN
978-3-030-28953-9
Edition Number
1
Number of Pages
XI, 439
Number of Illustrations
33 b/w illustrations, 119 illustrations in colour
Topics