Description
● 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
Author: Ghislain Landry Tsafack Chetsa
Publisher: Apress
Published: 08/28/2021
Pages: 140
Binding Type: Paperback
Weight: 0.50lbs
Size: 9.21h x 6.14w x 0.33d
ISBN13: 9781484272138
ISBN10: 1484272137
BISAC Categories:
- Computers | Artificial Intelligence | General
- Philosophy | Ethics & Moral Philosophy
- Computers | Security | General
About the Author
Ghislain Tsafack is Head of Data Science at Elemental Concept 2016 LTD (EC), where he leads the organization's AI strategy. As part of this, he leads the company's work in leveraging the latest advances in AI to help clients create value from their data and auditing AI systems developed by third parties on behalf of potential investors.
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.