TY  - BOOK
T1  - Explainable deep learning AI methods and challenges
A2  - Benois-Pineau, Jenny
A2  - Bourqui, Romain
A2  - Petkovic, Dragutin
A2  - Quenot, Georges
LA  - English
PP  - London, UK, San Diego, CA
PB  - Academic Press, an imprint of Elsevier
YR  - 2023
UL  - https://tuklas.up.edu.ph/Record/UP-8027390931311669105
AB  - 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. Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI. Explores the latest developments in general XAI methods for Deep Learning. Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing. Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
OP  - 329
CN  - Q 325.73 E97 2023
SN  - 9780323960984
KW  - Artificial intelligence : Deep learning (Machine learning) : Explanation-based learning.
ER  -