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Explainable AI within the digital tr...

Sayed-Mouchaweh, Moamar.

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Explainable AI within the digital transformation and cyber physical systems = XAI methods and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable AI within the digital transformation and cyber physical systems/ edited by Moamar Sayed-Mouchaweh.
Reminder of title:	XAI methods and applications /
other author:	Sayed-Mouchaweh, Moamar.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	x, 198 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-76409-8
ISBN:	9783030764098

Explainable AI within the digital transformation and cyber physical systems = XAI methods and applications /
Explainable AI within the digital transformation and cyber physical systemsXAI methods and applications /[electronic resource] :edited by Moamar Sayed-Mouchaweh. - Cham :Springer International Publishing :2021. - x, 198 p. :ill., digital ;24 cm.

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.

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 etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) 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.

ISBN: 9783030764098

Standard No.: 10.1007/978-3-030-76409-8doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335 / .E96 2021

Dewey Class. No.: 006.3

Explainable AI within the digital transformation and cyber physical systems = XAI methods and applications /
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505 0 $a 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.
520 $a 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 etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) 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.
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