Description
The Governance of Artificial Intelligence provides an essential approach to AI governance, including proactive and comprehensive strategies that efficiently balance innovation and ethical concerns. The book prioritizes social welfare and upholds human rights by maximizing the benefits of AI while reducing its negative aspects. Sections address the principles that govern artificial intelligence, data-related topics, AI algorithms, the issue of computing, applications, and AI governance. Throughout each section, the idea that it is essential to implement a versatile governance structure that incorporates several fields of study and encourages diversity is reinforced. Additionally, utilizing existing regulatory frameworks, ethical standards, and industry benchmarks is essential. 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
Table of Contents
1. IntroductionSECTION A. AI Values2. Risk Identification and Mitigation: Performance Risk Quantification3. Transparency: Accuracy vs Transparency4. Fairness: Avoidable and Unavoidable Algorithmic Bias and Discrimination5. Truth: Algorithmic Deception6. Inclusion7. Balancing risks and opportunities: Pareto OptimalitySECTION B. Data Governance CHAPTER 8. Data Acquisition9. Cross-Border Data Flow10. Synthetic Data11. Data Analysis12. Data StorageSECTION C. Algorithmic Governance13. Algorithmic Selection14. Algorithmic Design15. Algorithmic Training16. Algorithmic TestingSECTION D. Computing Governance17. Semiconductor Chips18. Edge AI19. Cloud Computing20. Ambient Computing21. Quantum Computing22. Computing Energy23. Computing WaterSECTION E. Applications24. Finance25. Health26. ConflictsSECTION F. AI Governance27. Human Behavior28. Mechanisms29. Policy and Regulations30. AI Standards31. AI Laws32. Conclusion
