The Governance of Artificial Intelligence stands out as a comprehensive guide that unifies the essential dimensions of artificial intelligence-values, data, algorithms, computing, applications, and governance-within a single volume. The book offers expert guidance on each of these topics, blending engineering insight with governance strategies. It proposes a holistic approach to AI governance, emphasizing the importance of proactive and balanced policies that foster innovation while safeguarding ethical standards. Prioritizing social welfare and human rights, this work advocates for maximizing…mehr
The Governance of Artificial Intelligence stands out as a comprehensive guide that unifies the essential dimensions of artificial intelligence-values, data, algorithms, computing, applications, and governance-within a single volume. The book offers expert guidance on each of these topics, blending engineering insight with governance strategies. It proposes a holistic approach to AI governance, emphasizing the importance of proactive and balanced policies that foster innovation while safeguarding ethical standards. Prioritizing social welfare and human rights, this work advocates for maximizing AI’s benefits and minimizing its risks through effective, integrative governance structures. Moreover, the book highlights the need for a versatile governance model that draws from various disciplines and champions diversity. It stresses the importance of leveraging existing regulatory frameworks, ethical guidelines, and industry standards, while encouraging active collaboration among governments, businesses, civil society, and academia. Structured into six sections and 33 chapters, the book systematically explores core principles, data concerns, algorithms, computing, practical applications, and governance challenges, making it a crucial resource for understanding the evolving landscape of AI oversight.
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.
Inhaltsangabe
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
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
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