- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
Generative AI technologies are reshaping the way we create, communicate, and compute, presenting both unprecedented opportunities and profound challenges. As these systems increasingly influence decisions that shape our lives, it becomes clear that performance metrics alone cannot define their success. A deeper understanding of the intersection between technical innovation and human values is essential to navigate this transformative era.
The Art and Ethics of Generative AI: Fairness, Transparency, and Human Values provides a balanced exploration of generative AI, combining technical insights with ethical considerations. It explains generative model architectures and learning paradigms in an accessible way, while addressing critical issues such as algorithmic bias, explainability challenges, and emerging regulatory approaches. Practical frameworks for responsible AI development are offered, alongside multidisciplinary perspectives that bridge technical and humanistic viewpoints. Abstract concepts are brought to life through practical illustrations, demonstrating how ethical considerations play out in real-world AI applications.
Designed for a wide audience, the book serves AI practitioners seeking actionable guidance for building responsible systems, researchers exploring nuanced ethical dimensions, educators and students looking for comprehensive learning materials, and policymakers needing governance insights. It is an essential resource for anyone engaged in the rapidly evolving AI ecosystem.
Contents
Chapter 1. Foundations of Generative AI. 1.1 AI and its Evolution. 1.2 Understanding Generative AI, Fairness, and Explainability. 1.3 Historical Development of Generative AI. 1.4 AI and Generative AI. 1.5 AI Pillars: ML, DL, GenAI and LLM. 1.6 Generative AI Blueprint. 1.7 Core Foundations of Generative AI: Mathematics, Models, and Metrics. 1.8 GenAI Engine : Transformers. 1.9 Loss Functions in Generative AI Models. 1.10 Metrics for Evaluating Generative Models. 1.11 Ethical and Societal Considerations. Exercise. References. Chapter 2.The Ethical Landscape of Generative AI. 2.1 The Promise of Generative AI. 2.2 Ethical Challenges and Implications. 2.3 Pros and Cons of AI Adoption across Industries. 2.4 Ethical Implications Across Industries. 2.5 Conclusion. Exercise. References. Chapter 3. Exploring AI Learning Paradigms: From Supervised to Generative Learning. 3.1 AI Learning Paradigms. 3.2 Supervised Learning. 3.3 Unsupervised Learning. 3.4 Semi-Supervised Learning. 3.5 Reinforcement Learning (RL). 3.6 Self-Supervised Learning. 3.7 Generative Learning. 3.8 Summarization and Comparison of Different Learning Paradigms. 3.10 Case Studies. Exercise. References. Chapter 4. Generative AI: Models and Architecture. 4.1 GenAI Five-Layer Framework Architecture. 4.2 Generative AI Architecture Models. 4.3 Real-World Applications and Use Cases of Generative AI. 4.4 Conclusion. Exercise. References. Chapter 5. Fairness in Generative AI. 5.1 Bias in Training Data Description. 5.2. Approaches to Promote Fairness. 5.3 Fairness Measuring Techniques. 5.4 Case Studies of Bias in Generative AI. 5.5 Metrics to Evaluate Fairness in Generative AI. 5.6 Legal, Ethical, And Policy Dimensions Of Fairness In Generative AI. Exercise. References. Chapter 6. Opening the Black Box: Explainability in GAI. 6.1. Importance Of Explainability in GAI. 6.2. Challenges In Explaining GAI. 6.3. Methods of Explainability in GAI. 6.4. Research Aspects in Explainability for GAI. Exercise. References. Chapter7. Bridging Fairness and Explainability in AI. 7.1 The Interplay between Bias and Opacity. 7.2 Explainability as a Tool for Fairness Audits. 7.3 Socio-Technical Perspective on Fair and Explainable AI. 7.4 Technical Methods Bridging Fairness and Explainability. 7.5 Real-World Case Studies. 7.6 Evaluation Metric and Benchmarking Fair-Explainable AI System. Exercise. References. Chapter 8. Challenges and Limitations. 8.1 Trade-offs Between Fairness, Explainability, and Accuracy. 8.2 Computational Barriers. 8.3 Legal Barriers. 8.4 Social Barriers. 8.5 Standardization Gaps in Evaluation. 8.6 Conclusion and Future Outlook. Exercise. References. Chapter 9. Future Directions and Responsible AI. 9.1 Research Opportunities: Hybrid Models and Symbolic Reasoning. 9.2 Scalable Solutions for Fairness-Aware Systems. 9.3 Multidisciplinary Frameworks for Ethical AI. 9.4 Governance, Policy, and User-Centric Design. 9.5 Future Outlook and Open Challenges. 9.6 Conclusion. Exercise. References
