Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Editors: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Muller, K.-R. (Eds.)
Free Preview- 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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- About this book
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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)
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Towards Explainable Artificial Intelligence
Pages 5-22
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Transparency: Motivations and Challenges
Pages 23-40
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Interpretability in Intelligent Systems – A New Concept?
Pages 41-49
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Understanding Neural Networks via Feature Visualization: A Survey
Pages 55-76
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Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
Pages 77-95
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Table of contents (22 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
- Editors
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- Wojciech Samek
- Grégoire Montavon
- Andrea Vedaldi
- Lars Kai Hansen
- Klaus-Robert Muller
- 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
