
The Governance of Artificial Intelligence
- 1st Edition - April 1, 2026
- Author: Tshilidzi Marwala
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 6 3 2 2 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 6 3 2 3 - 8
Artificial Intelligence governance is complicated by the fast pace of technological progress, the opaqueness of AI algorithms, worries about bias and impartiality, the requirement… Read more
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Artificial Intelligence governance is complicated by the fast pace of technological progress, the opaqueness of AI algorithms, worries about bias and impartiality, the requirement for accountability in AI-based decisions, and the global nature of AI development and deployment. The issue of AI governance is a developing one, and The Governance of AI is the first book that covers all the key topics in one book: AI values, Data, Algorithms, Computing, Applications, and Governance. The Governance of AI provides top-level guidance on all these topics from an engineering and governance perspective, while proposing a unifying framework for AI governance. An essential approach to AI governance is a proactive and comprehensive strategy that efficiently balances innovation and ethical concerns. The strategy presented in this book prioritizes social welfare and upholds human rights by maximizing the benefits of AI while reducing its negative aspects. In order to address these issues, it is essential to implement a versatile governance structure that incorporates several fields of study and encourages diversity. Additionally, utilizing existing regulatory frameworks, ethical standards, and industry benchmarks is essential. Moreover, it is crucial to integrate cooperation between governments, economic organizations, civil society, and the academic community under a multi-stakeholder framework to promote transparency, accountability, and public trust in AI systems. Furthermore, it is imperative to cultivate global cooperation in regulating AI because AI technology and its impacts extend beyond national boundaries. AI governance involves establishing worldwide norms and standards that encourage coordinating governance efforts while recognizing cultural and geographical differences. The Governance of AI is structured into six distinct sections and comprises 33 chapters. The first section comprises the chapters that address the principles that govern artificial AI. The second section has chapters that specifically address data-related topics. The AI algorithms are discussed in the third section. The fourth section has chapters that address the issue of computing. The fifth section has chapters that specifically address applications. The sixth section has chapters that address the topic of AI governance.
- Presents the critical issue of values in AI use, which is important given the proliferation of generative AI
- Demonstrates how to handle data and apply AI, including case studies for better understanding of the topics covered
- Deals with the complex problem of governing data, algorithms, computing, and applications to health, finance, and conflicts
- Companion website includes a series of videos from the author, providing supplementary information and guidance for understanding key concepts
Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the field of IT management and public policy. The primary audience also includes data analysts, software engineers, as well as researchers and professionals across the fields of science and engineering
1. Introduction
SECTION A. AI Values
2. Risk Identification and Mitigation: Performance Risk Quantification
3. Transparency: Accuracy vs Transparency
4. Fairness: Avoidable and Unavoidable Algorithmic Bias and Discrimination
5. Truth: Algorithmic Deception
6. Inclusion
7. Balancing risks and opportunities: Pareto Optimality
SECTION B. Data Governance CHAPTER 8. Data Acquisition
9. Cross-Border Data Flow
10. Synthetic Data
11. Data Analysis
12. Data Storage
SECTION C. Algorithmic Governance
13. Algorithmic Selection
14. Algorithmic Design
15. Algorithmic Training
16. Algorithmic Testing
SECTION D. Computing Governance
17. Semiconductor Chips
18. Edge AI
19. Cloud Computing
20. Ambient Computing
21. Quantum Computing
22. Computing Energy
23. Computing Water
SECTION E. Applications
24. Finance
25. Health
26. Conflicts
SECTION F. AI Governance
27. Human Behavior
28. Mechanisms
29. Policy and Regulations
30. AI Standards
31. AI Laws
32. Conclusion
SECTION A. AI Values
2. Risk Identification and Mitigation: Performance Risk Quantification
3. Transparency: Accuracy vs Transparency
4. Fairness: Avoidable and Unavoidable Algorithmic Bias and Discrimination
5. Truth: Algorithmic Deception
6. Inclusion
7. Balancing risks and opportunities: Pareto Optimality
SECTION B. Data Governance CHAPTER 8. Data Acquisition
9. Cross-Border Data Flow
10. Synthetic Data
11. Data Analysis
12. Data Storage
SECTION C. Algorithmic Governance
13. Algorithmic Selection
14. Algorithmic Design
15. Algorithmic Training
16. Algorithmic Testing
SECTION D. Computing Governance
17. Semiconductor Chips
18. Edge AI
19. Cloud Computing
20. Ambient Computing
21. Quantum Computing
22. Computing Energy
23. Computing Water
SECTION E. Applications
24. Finance
25. Health
26. Conflicts
SECTION F. AI Governance
27. Human Behavior
28. Mechanisms
29. Policy and Regulations
30. AI Standards
31. AI Laws
32. Conclusion
- Edition: 1
- Published: April 1, 2026
- Language: English
TM
Tshilidzi Marwala
Dr. Tshilidzi Marwala is the United Nations (UN) University Rector and UN Under-Secretary-General based in Tokyo, Japan. He was the Vice-Chancellor and Principal of the University of Johannesburg and a trustee of the Nelson Mandela Foundation. He is a member of the American Academy of Arts and Sciences, The World Academy of Sciences (TWAS) and the African Academy of Sciences. He has supervised 37 doctoral students from more than 20 countries in Africa, Asia, Europe, the Middle East, and the Americas. Dr. Marwala holds a Bachelor of Science in Mechanical Engineering (magna cum laude) from Case Western Reserve University, USA, and a Ph.D. in Artificial Intelligence from the University of Cambridge, UK. He has published 27 books on Artificial Intelligence, one translated into Chinese, over 500 articles in journals, proceedings, book chapters and newspapers, and he holds five international patents. He is the author of Hamiltonian Monte Carlo Methods in Machine Learning and Rational Machines and Artificial Intelligence from Elsevier/Academic Press.
Affiliations and expertise
Rector of the United Nations University and Under-Secretary-General of the United Nations, Tokyo, Japan