Explainable Deep Learning AI: Methods and Challenges

Explainable Deep Learning AI: Methods and Challenges provides an overview of the XAI area, covering general XAI methods, specific technical works, and user-oriented evaluation approaches. It also explores deep learning, which is necessary for various AI applications, and explains methods such as back-propagation and perturbation-based methods.

Format: Paperback / softback
Length: 346 pages
Publication date: 24 February 2023
Publisher: Elsevier Science & Technology

Explainable Deep Learning AI: Methods and Challenges is a comprehensive resource that delves into the realm of XAI, providing an overview of this emerging field while showcasing innovative technical approaches and applications aimed at addressing the challenges of explainability in deep learning AI systems. The book begins by providing a foundational understanding of XAI, encompassing its principles, methodologies, and applications. It then proceeds to explore a diverse range of specific technical works and methodologies, encompassing both general XAI methods and specialized XAI applications. Furthermore, the book delves into user-oriented evaluation approaches, ensuring that the developed solutions are both effective and accessible to end users.

In addition to its comprehensive coverage, Explainable Deep Learning AI: Methods and Challenges also explores the fundamental categories of explainable AI, particularly focusing on deep learning, which has become an indispensable component in various applications of artificial intelligence. The book provides detailed explanations of methods such as back-propagation and perturbation-based methods, which are widely employed in deep learning for tasks such as data classification. It also showcases their application to diverse datasets and scenarios, highlighting their effectiveness and versatility.

By presenting the latest works of leading researchers in the XAI area, Explainable Deep Learning AI: Methods and Challenges serves as a valuable resource for researchers, practitioners, and students alike. It offers a comprehensive understanding of XAI, facilitates the exploration of innovative technical solutions, and promotes the development of more user-friendly and interpretable deep learning AI systems. As the field of XAI continues to evolve and expand, this book will remain an essential reference for anyone seeking to advance the understanding and application of explainable deep learning in their respective domains.

Weight: 698g
Dimension: 189 x 234 x 23 (mm)
ISBN-13: 9780323960984