東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Ethics in artificial intelligence = bias, fairness and beyond /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ethics in artificial intelligence/ edited by Animesh Mukherjee ... [et al.].
Reminder of title:	bias, fairness and beyond /
other author:	Mukherjee, Animesh.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xii, 143 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Making Socially Sustainable and Ethical AI Systems: Integrating Impact Assessment in the Co-Design Approach -- Discrimination in Advertising and Personalization -- Biases and Ethical Considerations in ML Pipelines In The Computational Social Sciences -- Operationalizing Fairness -- Achieving Group and Individual Fairness in Clustering Algorithms -- Fair Allocation of Structured Set Systems -- Algorithmic Fairness for Decisions Across Time -- Algorithmic Fairness in Multi-stakeholder Platforms -- Fairness Testing, Debugging and Repairing -- Interpretability of Machine Learning Models.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Moral and ethical aspects. -
Online resource:	https://doi.org/10.1007/978-981-99-7184-8
ISBN:	9789819971848

Ethics in artificial intelligence = bias, fairness and beyond /
Ethics in artificial intelligencebias, fairness and beyond /[electronic resource] :edited by Animesh Mukherjee ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xii, 143 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 11231860-9503 ;. - Studies in computational intelligence ;v. 1123..

Making Socially Sustainable and Ethical AI Systems: Integrating Impact Assessment in the Co-Design Approach -- Discrimination in Advertising and Personalization -- Biases and Ethical Considerations in ML Pipelines In The Computational Social Sciences -- Operationalizing Fairness -- Achieving Group and Individual Fairness in Clustering Algorithms -- Fair Allocation of Structured Set Systems -- Algorithmic Fairness for Decisions Across Time -- Algorithmic Fairness in Multi-stakeholder Platforms -- Fairness Testing, Debugging and Repairing -- Interpretability of Machine Learning Models.

This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments - the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in software testing/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.

ISBN: 9789819971848

Standard No.: 10.1007/978-981-99-7184-8doiSubjects--Topical Terms:

961670
Artificial intelligence
--Moral and ethical aspects.

LC Class. No.: Q334.7

Dewey Class. No.: 174.90063

Ethics in artificial intelligence = bias, fairness and beyond /
LDR:02893nmm a2200337 a 4500 001 2390053
003 DE-He213
005 20231229091443.0
006 m d
007 cr nn 008maaau
008 250916s2023 si s 0 eng d
020 $a 9789819971848 $q (electronic bk.)
020 $a 9789819971831 $q (paper)
024 7 $a 10.1007/978-981-99-7184-8 $2 doi
035 $a 978-981-99-7184-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q334.7
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 174.90063 $2 23
090 $a Q334.7 $b .E84 2023
245 0 0 $a Ethics in artificial intelligence $h [electronic resource] : $b bias, fairness and beyond / $c edited by Animesh Mukherjee ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xii, 143 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1123
505 0 $a Making Socially Sustainable and Ethical AI Systems: Integrating Impact Assessment in the Co-Design Approach -- Discrimination in Advertising and Personalization -- Biases and Ethical Considerations in ML Pipelines In The Computational Social Sciences -- Operationalizing Fairness -- Achieving Group and Individual Fairness in Clustering Algorithms -- Fair Allocation of Structured Set Systems -- Algorithmic Fairness for Decisions Across Time -- Algorithmic Fairness in Multi-stakeholder Platforms -- Fairness Testing, Debugging and Repairing -- Interpretability of Machine Learning Models.
520 $a This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments - the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in software testing/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.
650 0 $a Artificial intelligence $x Moral and ethical aspects. $3 961670
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Mukherjee, Animesh. $3 1084542
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1123. $3 3756152
856 4 0 $u https://doi.org/10.1007/978-981-99-7184-8
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)