Towards Sustainable Artificial Intelligence: A Framework to Create Value and Understand Risk
Ghislain Landry Tsafack Chetsa
Résumé
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.
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.Chapter 2 Ethics of the Data Science Practice Chapter goal: Reviews the human factor pillar of artificial intelligence, the relevance of ethics in AI and the source of ethical hazards in AI 2.1 Introduction 2.2 Ethics and their relevance to AI 2.3 Ethical nature of AI inferencing capability 2.4 Data - The business asset 2.5 AI regulatory outlook 2.6 Conclusion
Chapter 3 Overview of the Sustainable Artificial Intelligence Framework (SAIF) Chapter goal: Summarises the SAIF framework for the development and deployment of AI applications
Chapter 4 Intra-organizational understanding of AI: Towards Transparency Chapter goal: Discusses the need for understanding AI at the organization's level and introduces concepts of AI governance 4.1 Introduction 4.2 Data Science Development Process 4.3 AI development process Controls 4.4 Governance 4.4.1 Expectations from AI governance 4.4.2 People and Values 4.4.3 Assessment of AI governance arrangements 4.5 Conclusion
Chapter 5 AI Performance Measurement: Think business values and objectives Chapter goal: Summarises performance metrics for evaluating AI systems and introduces a framework to account for the human factor of AI 5.1 Introduction 5.2 AI performance metrics overview 5.2.1 Supervised problems 5.2.2 Unsupervised problems 5.3 Beyond traditional AI performance metrics 5.3.1 Soft performance metrics 5.3.2 From AI performance metrics to business objectives 5.4 Conclusion
Chapter 6 SAIF in Action Chapter goal: This chapter illustrates how SAIF would work in practice through use cases
Chapter 7 Alternatives avenues for regulating AI systems Chapter goal: Draws from experiences in academic, Telecom/Utility, and healthcare sectors to explore and examine the need for industry specific regulations.
Chapter 8 AI decision-making - from expectations to reality: The use case of healthcare Chapter goal: Explores the use of artificial intelligence in the healthcare, its practical limitations an implications
Chapter 9 Conclusions and discussion Chapter goal: Presents concluding remarks and discuss current lack of standards 9.1 Conclusions 9.2 Need for standards and definitions
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 Superieure of Lyon, France.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Apress |
| Auteur(s) | Ghislain Landry Tsafack Chetsa |
| Parution | 30/07/2021 |
| Nb. de pages | 140 |
| EAN13 | 9781484272138 |
Avantages Eyrolles.com
Consultez aussi
- Les meilleures ventes en Graphisme & Photo
- Les meilleures ventes en Informatique
- Les meilleures ventes en Construction
- Les meilleures ventes en Entreprise & Droit
- Les meilleures ventes en Sciences
- Les meilleures ventes en Littérature
- Les meilleures ventes en Arts & Loisirs
- Les meilleures ventes en Vie pratique
- Les meilleures ventes en Voyage et Tourisme
- Les meilleures ventes en BD et Jeunesse
