Human-Centered Artificial Intelligence: Research and Applications presents current theories, fundamentals, techniques and diverse applications of human-centered AI. Sections address the question, "are AI models explainable, interpretable and understandable?, introduce readers to the design and development process, including mind perception and human interfaces, explore various applications of human-centered AI, including human-robot interaction, healthcare and decision-making, and more. As human-centered AI aims to push the boundaries of previously limited AI solutions to bridge the gap between machine and human, this book is an ideal update on the latest advances.
1. Human-Centered Artificial Intelligence - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. Foreword - p. Page vi 8. Preface - p. Page vi 9. List of Illustrations - p. Page viii 10. List of Tables - p. Page ix 11. Chapter 1 : Are AI models explainable, interpretable, and understandable? - p. Page ix 12. Chapter 2 : Explanation using model-agnostic methods - p. Page 15 13. Chapter 3 : Explanation using examples - p. Page 31 14. Chapter 4 : Explanation of ensemble models - p. Page 48 15. Chapter 5 : Explanation of deep learning models - p. Page 71 16. Chapter 6 : AI as an explanation agent and user-centered explanation interfaces for trust in AI-based systems - p. Page 86 17. Chapter 7 : Anthropomorphism in human-centered AI: Determinants and consequences of applying human knowledge to AI agents - p. Page 101 18. Chapter 8 : Designing a pragmatic explanation for the XAI system based on the user's context and background knowledge - p. Page 111 19. Chapter 9 : Interactive reinforcement learning and error-related potential classification for implicit feedback - p. Page 125 20. Chapter 10 : Reinforcement learning in EEG-based human-robot interaction - p. Page 140 21. Chapter 11 : Shopping with AI: Consumers' perceived autonomy in the age of AI - p. Page 153 22. Chapter 12 : Use of deep learning techniques in EEG-based BCI applications - p. Page 167 23. Chapter 13 : AI in human behavior analysis - p. Page 187 24. Chapter 14 : AI in nondestructive condition assessment of concrete structures: Detecting internal defects and improving prediction performance using prediction integration and data proliferation techniques - p. Page 203 25. Chapter 15 : Ethics of AI in organizations - p. Page 217 26. Chapter 16 : Designing XAI from policy perspectives - p. Page 236 27. Chapter 17 : Responsible AI and algorithm governance: An institutional perspective - p. Page 248 28. Author Index - p. Page 268 29. Subject Index - p. Page 284
Human-Centered Artificial Intelligence: Research and Applications presents current theories, fundamentals, techniques and diverse applications of human-centered AI. Sections address the question, "are AI models explainable, interpretable and understandable?, introduce readers to the design and development process, including mind perception and human interfaces, explore various applications of human-centered AI, including human-robot interaction, healthcare and decision-making, and more. As human-centered AI aims to push the boundaries of previously limited AI solutions to bridge the gap between machine and human, this book is an ideal update on the latest advances.
1. Human-Centered Artificial Intelligence - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. Foreword - p. Page vi 8. Preface - p. Page vi 9. List of Illustrations - p. Page viii 10. List of Tables - p. Page ix 11. Chapter 1 : Are AI models explainable, interpretable, and understandable? - p. Page ix 12. Chapter 2 : Explanation using model-agnostic methods - p. Page 15 13. Chapter 3 : Explanation using examples - p. Page 31 14. Chapter 4 : Explanation of ensemble models - p. Page 48 15. Chapter 5 : Explanation of deep learning models - p. Page 71 16. Chapter 6 : AI as an explanation agent and user-centered explanation interfaces for trust in AI-based systems - p. Page 86 17. Chapter 7 : Anthropomorphism in human-centered AI: Determinants and consequences of applying human knowledge to AI agents - p. Page 101 18. Chapter 8 : Designing a pragmatic explanation for the XAI system based on the user's context and background knowledge - p. Page 111 19. Chapter 9 : Interactive reinforcement learning and error-related potential classification for implicit feedback - p. Page 125 20. Chapter 10 : Reinforcement learning in EEG-based human-robot interaction - p. Page 140 21. Chapter 11 : Shopping with AI: Consumers' perceived autonomy in the age of AI - p. Page 153 22. Chapter 12 : Use of deep learning techniques in EEG-based BCI applications - p. Page 167 23. Chapter 13 : AI in human behavior analysis - p. Page 187 24. Chapter 14 : AI in nondestructive condition assessment of concrete structures: Detecting internal defects and improving prediction performance using prediction integration and data proliferation techniques - p. Page 203 25. Chapter 15 : Ethics of AI in organizations - p. Page 217 26. Chapter 16 : Designing XAI from policy perspectives - p. Page 236 27. Chapter 17 : Responsible AI and algorithm governance: An institutional perspective - p. Page 248 28. Author Index - p. Page 268 29. Subject Index - p. Page 284
