Название: Explainable Deep Learning AI: Methods and ChallengesАвтор: Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 348
Язык: английский
Формат: pdf (true), epub
Размер: 51.3 MB
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 (AI).
Artificial Intelligence (AI) techniques, especially those based on Deep Learning (DL), have become extremely effective on a very large variety of tasks, sometimes performing even better than human experts. However, they also have a number of problems: they generally operate in mostly opaque and/or intractable ways, their very good performance is only statistical and they can fail even on apparently obvious cases, they can make biased decisions, and they can be quite easily manipulated through adversarial attacks, to cite a few. These limitations prevent their adoption in applications of great economic or societal interest, especially for critical or sensible applications like autonomous driving, medical diagnosis, or loan approvals.
Considering this, a lot of research has been conducted in order to increase the trust-worthiness of DL-based AI systems by providing explanations understandable by human users for the decisions made by these systems. The aim of this book is to present recent and original contributions covering the main approaches in the domain of explainable DL, either for expert or for layman users. Two main types of approaches are presented: the “post hoc” or “model agnostic” ones, in which the operation of an already available “black box” system is modeled and explained, and the intrinsic ones, in which systems are specifically designed as “white boxes” with an interpretable mode of operation.
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 (NLP)
Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
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