Explainable AI Within the Digital Transformation and Cyber Physical Systems : XAI Methods and Applications / edited by Moamar Sayed-Mouchaweh.

Format:

Book

Contributor:

Sayed-Mouchaweh, Moamar, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Telecommunication.

Computational intelligence.

Artificial intelligence.

Data mining.

Quantitative research.

Communications Engineering, Networks.

Computational Intelligence.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Local Subjects:

Communications Engineering, Networks.

Computational Intelligence.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Physical Description:

1 online resource (X, 198 pages) : 69 illustrations

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities et cetera) with different levels of technical knowledge (managers, engineers, technicians, et cetera) in different application domains. Secondly, that it is important to develop an evaluation framework and standards in order to measure the effectiveness of the provided explanations at the human and the technical levels. This book gathers research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefits and requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions. Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems; Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems; Presents examples and case studies in order to increase transparency and understanding of the methodological concepts.

Contents:

Introduction

Part 1 Methods used to generate explainable models

Explainable Artificial Intelligence (XAI)

intrinsic explainable models

model-agnostic methods

Part 2 Evaluation layout and meaningful criteria

expressive power

portability evaluation layout

accuracy evaluation layout

algorithmic complexity

fidelity evaluation

stability evaluation

representativeness evaluation layout

local/global explanation

Part 3 XAI applications within the context of digital transformation and cyber-physical systems

applications of XAI in decision support tools

smart energy management

finance

telemedicine and healthcare

critical systems

e-government

Conclusion.

Other Format:

Printed edition:

ISBN:

978-3-030-76409-8

9783030764098

Access Restriction:

Restricted for use by site license.