Overview
- Identify ethical hazards throughout the development and deployment of AI systems
- Understand how to audit an organization’s AI systems
- Explore how you can assess the impact of AI on an organization’s ability to create value in the short, medium, and long term
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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 deploymentof 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.
What You Will Learn
- See the relevance of ethics to the practice of data science and AI
- Examine the elements that enable AI within an organization
- Discover the challenges of developing AI systems that meet certain human or specific standards
- Explore the challenges of AI governance
- Absorb the key factors to consider when evaluating AI systems
Who This Book Is For
Decision makers such as government officials, members of the C-suite and other business managers, and data scientists as well as any technology expert aspiring to a data-related leadership role.Authors and Affiliations
About the author
Ghislain’s work in the healthcare industry at EC involves supporting the development of data related healthcare products for his clients. This made him appreciate the challenges and the complexity of developing AI systems that people trust to make the right decision for them and stimulated him to write this book.
Before joining EC Ghislain held positions as data scientist in the telecommunications and energy sectors. Prior to this, Ghislain worked as an academic at the French National Institute for Research and Automation (INRIA) and the University of Lyon 1. His work primarily focused on analyzing the behaviors of high performance systems to improve their energy efficiency and gave him the opportunity to co-author several scientific books presenting methodologies for improving the energy efficiency for large scale computing infrastructures. He holds a PhD in computer science from Ecole Normale Supérieure of Lyon, France.
Bibliographic Information
Book Title: Towards Sustainable Artificial Intelligence
Book Subtitle: A Framework to Create Value and Understand Risk
Authors: Ghislain Landry Tsafack Chetsa
DOI: https://doi.org/10.1007/978-1-4842-7214-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Ghislain Landry Tsafack Chetsa 2021
Softcover ISBN: 978-1-4842-7213-8Published: 31 July 2021
eBook ISBN: 978-1-4842-7214-5Published: 30 July 2021
Edition Number: 1
Number of Pages: XIII, 140
Number of Illustrations: 8 b/w illustrations
Topics: Artificial Intelligence, Engineering Ethics, Systems and Data Security, Data Structures and Information Theory, Machine Learning