The recent focus of Artificial Intelligence (AI) researchers and practitioners on supervised learning approaches, particularly on Deep Learning, has resulted in a considerable increase of performance of AI systems, but this has raised the question of the trustfulness and explainability of their predictions for human decision makers and adopters. Explainable AI (XAI) is addressing this challenge by developing methods to understand and explain to humans how these systems produce their decisions. This book presents the latest works of leading researchers in XAI area and will offer the reader, besides an overview of the XAI area, several novel technical methods and applications that address explainability challenges for Deep Learning AI systems.
The book starts with the overviewing the XAI area, then in 13 chapters covers a number of specific technical works and approaches to XAI for Deep learning, ranging from general XAI methods, to specific XAI applications, and finally with user-oriented evaluation approaches.
It explores the main categories of methods of explainable AI - Deep Learning, which become the necessary condition in various applications of Artificial Intelligence, following a methodological approach. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of the data classification is presented. It also addresses important questions on evaluation by users.
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