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Towards sustainable artificial intel...
~
Chetsa, Ghislain Landry Tsafack.
Towards sustainable artificial intelligencea framework to create value and understand risk /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Towards sustainable artificial intelligenceby Ghislain Landry Tsafack Chetsa.
其他題名:
a framework to create value and understand risk /
作者:
Chetsa, Ghislain Landry Tsafack.
出版者:
Berkeley, CA :Apress :2021.
面頁冊數:
xiii, 140 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Artificial intelligenceMoral and ethical aspects.
電子資源:
https://doi.org/10.1007/978-1-4842-7214-5
ISBN:
9781484272145$q(electronic bk.)
Towards sustainable artificial intelligencea framework to create value and understand risk /
Chetsa, Ghislain Landry Tsafack.
Towards sustainable artificial intelligence
a framework to create value and understand risk /[electronic resource] :by Ghislain Landry Tsafack Chetsa. - Berkeley, CA :Apress :2021. - xiii, 140 p. :ill., digital ;24 cm.
Chapter 1: AI in our Society -- Chapter 2 Ethics of the Data Science Practice -- Chapter 3 Sustainable AI Framework (SAIF): Overview of SAIF framework -- Chapter 4 Intra-organizational understanding of AI: Towards Transparency -- Chapter 5 AI Performance Measurement: Think business values -- Chapter 6 SAIF in Action: A Case Study -- Chapter 7 Alternative Avenues for Regulating AI Development -- Chapter 8 AI in the Medical Decision Context: The use case of healthcare -- Chapter 9 Conclusions and discussion.
So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization's demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles. This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems. The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term.
ISBN: 9781484272145$q(electronic bk.)
Standard No.: 10.1007/978-1-4842-7214-5doiSubjects--Topical Terms:
611796
Artificial intelligence
--Moral and ethical aspects.
LC Class. No.: Q334.7
Dewey Class. No.: 174.90063
Towards sustainable artificial intelligencea framework to create value and understand risk /
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Chapter 1: AI in our Society -- Chapter 2 Ethics of the Data Science Practice -- Chapter 3 Sustainable AI Framework (SAIF): Overview of SAIF framework -- Chapter 4 Intra-organizational understanding of AI: Towards Transparency -- Chapter 5 AI Performance Measurement: Think business values -- Chapter 6 SAIF in Action: A Case Study -- Chapter 7 Alternative Avenues for Regulating AI Development -- Chapter 8 AI in the Medical Decision Context: The use case of healthcare -- Chapter 9 Conclusions and discussion.
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So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization's demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles. This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems. The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term.
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