The Governance of Artificial Intelligence
Autor Tshilidzi Marwalaen Limba Engleză Paperback – apr 2026
Moreover, the book maintains that 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. Because of 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, it is imperative to cultivate global cooperation in regulating AI as 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.
- 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
- Includes a companion website with a series of videos from the author, providing supplementary information and guidance for understanding key concepts
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Specificații
ISBN-13: 9780443363221
ISBN-10: 0443363226
Pagini: 250
Dimensiuni: 216 x 276 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0443363226
Pagini: 250
Dimensiuni: 216 x 276 mm
Editura: ELSEVIER SCIENCE
Cuprins
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