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Ethics in artificial intelligence = bias, fairness and beyond /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Ethics in artificial intelligence/ edited by Animesh Mukherjee ... [et al.].
其他題名:	bias, fairness and beyond /
其他作者:	Mukherjee, Animesh.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xii, 143 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Artificial intelligence - Moral and ethical aspects. -
電子資源:	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 /
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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.
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