This book provides a comprehensive exploration of human-in-the-loop artificial intelligence (AI), covering topics that range from its fundamental concepts to data quality control, learning methods, explainability, incentive design, fairness, and ultimately human-AI collaboration.
The first half of the book organizes the theoretical foundations of human-in-the-loop AI and the design principles that support them, while the second half discusses how these ideas can be applied in real-world systems and applications, as well as the broader social implications of human-in-the-loop AI. Each chapter is self-contained and can be read independently, depending on the reader's interests and needs.
This book is suitable for researchers, practitioners, and students who are working on a wide variety of topics in AI.