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Explainable AIinterpreting, explaini...

Samek, Wojciech.

Explainable AIinterpreting, explaining and visualizing deep learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable AIedited by Wojciech Samek ... [et al.].
Reminder of title:	interpreting, explaining and visualizing deep learning /
other author:	Samek, Wojciech.
Published:	Cham :Springer International Publishing :2019.
Description:	xi, 439 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-030-28954-6
ISBN:	9783030289546$q(electronic bk.)

Explainable AIinterpreting, explaining and visualizing deep learning /
Explainable AIinterpreting, explaining and visualizing deep learning /[electronic resource] :edited by Wojciech Samek ... [et al.]. - Cham :Springer International Publishing :2019. - xi, 439 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,117000302-9743 ;. - Lecture notes in computer science ;4891..

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. Forsensitive 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.

ISBN: 9783030289546$q(electronic bk.)

Standard No.: 10.1007/978-3-030-28954-6doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .E86 2019

Dewey Class. No.: 006.3

Explainable AIinterpreting, explaining and visualizing deep learning /
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