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.
1. Chapter 1. Introduction to the governance of artificial intelligence - p. 1 2. Section I: Governance of AI values - p. S17 3. Chapter 2. Truth and accuracy - p. 19 4. Chapter 3. Transparency and openness - p. 31 5. Chapter 4. Safety and security - p. 43 6. Chapter 5. Diversity and inclusion - p. 55 7. Chapter 6. Privacy and ethics - p. 69 8. Section II: Data governance - p. S81 9. Chapter 7. Data acquisition - p. 83 10. Chapter 8. Cross-border data flow - p. 97 11. Chapter 9. Synthetic data - p. 113 12. Chapter 10. Data storage - p. 129 13. Section III: Algorithmic governance - p. S145 14. Chapter 11. Algorithmic selection - p. 147 15. Chapter 12. Algorithmic design - p. 161 16. Chapter 13. Algorithmic training - p. 175 17. Chapter 14. Algorithmic validation - p. 187 18. Chapter 15. Algorithmic testing - p. 201 19. Section IV: Computing governance - p. S213 20. Chapter 16. Semiconductor chips - p. 215 21. Chapter 17. Edge artificial intelligence - p. 229 22. Chapter 18. Cloud artificial intelligence - p. 241 23. Chapter 19. Ambient intelligence - p. 257 24. Chapter 20. Quantum intelligence - p. 271 25. Chapter 21. Computing energy and water - p. 285 26. Chapter 22. Materials selection - p. 299 27. Section V: Mathematical implications for AI governance - p. S311 28. Chapter 23. Maximum likelihood estimation and rare events - p. 313 29. Chapter 24. Bayesian approach and artificial intelligence risk - p. 323 30. Chapter 25. Subjectivity and consistency of artificial intelligence - p. 337 31. Chapter 26. Federated learning, privacy and accuracy - p. 349 32. Chapter 27. Backpropagation and artificial intelligence truth - p. 363 33. Chapter 28. Computational efficiency - p. 373 34. Chapter 29. Artificial intelligence as a low-precision technology - p. 387 35. Chapter 30. Conclusion: shaping a future where artificial intelligence benefits humanity - p. 401 36. Index - p. 405
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.
1. Chapter 1. Introduction to the governance of artificial intelligence - p. 1 2. Section I: Governance of AI values - p. S17 3. Chapter 2. Truth and accuracy - p. 19 4. Chapter 3. Transparency and openness - p. 31 5. Chapter 4. Safety and security - p. 43 6. Chapter 5. Diversity and inclusion - p. 55 7. Chapter 6. Privacy and ethics - p. 69 8. Section II: Data governance - p. S81 9. Chapter 7. Data acquisition - p. 83 10. Chapter 8. Cross-border data flow - p. 97 11. Chapter 9. Synthetic data - p. 113 12. Chapter 10. Data storage - p. 129 13. Section III: Algorithmic governance - p. S145 14. Chapter 11. Algorithmic selection - p. 147 15. Chapter 12. Algorithmic design - p. 161 16. Chapter 13. Algorithmic training - p. 175 17. Chapter 14. Algorithmic validation - p. 187 18. Chapter 15. Algorithmic testing - p. 201 19. Section IV: Computing governance - p. S213 20. Chapter 16. Semiconductor chips - p. 215 21. Chapter 17. Edge artificial intelligence - p. 229 22. Chapter 18. Cloud artificial intelligence - p. 241 23. Chapter 19. Ambient intelligence - p. 257 24. Chapter 20. Quantum intelligence - p. 271 25. Chapter 21. Computing energy and water - p. 285 26. Chapter 22. Materials selection - p. 299 27. Section V: Mathematical implications for AI governance - p. S311 28. Chapter 23. Maximum likelihood estimation and rare events - p. 313 29. Chapter 24. Bayesian approach and artificial intelligence risk - p. 323 30. Chapter 25. Subjectivity and consistency of artificial intelligence - p. 337 31. Chapter 26. Federated learning, privacy and accuracy - p. 349 32. Chapter 27. Backpropagation and artificial intelligence truth - p. 363 33. Chapter 28. Computational efficiency - p. 373 34. Chapter 29. Artificial intelligence as a low-precision technology - p. 387 35. Chapter 30. Conclusion: shaping a future where artificial intelligence benefits humanity - p. 401 36. Index - p. 405
