Explainable Deep Learning AI

Methods and Challenges
Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.

Contributor: Dragutin Petkovic (Edited by), Georges Quenot (Edited by), Jenny Benois-Pineau (Edited by), Romain Bourqui (Edited by)

Pages: 346

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