Human-in-the-loop Artificial Intelligence

Human-in-the-loop Artificial Intelligence

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 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.

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 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.

Highlights

• Provides a comprehensive exploration of human-in-the-loop artificial intelligence Details the theoretical foundations and the design principles Discusses how human-in-the-loop AI can be applied in real-world systems and applications

Über den Autor

Hisashi Kashima is a Professor at the Graduate School of Informatics, Kyoto University. He previously worked as a research staff member at IBM Research Tokyo (1999–2009) and as an Associate Professor at The University of Tokyo (2009–2014). His research focuses on the foundations of machine learning and data mining, as well as their applications in a wide range of domains, including marketing, bioinformatics, and industrial analytics. He is also interested in human-in-the-loop approaches and human–AI collaboration. He earned his B.S., M.S., and Ph.D. from Kyoto University.

Satoshi Oyama is a Professor at the Graduate School of Data Science, Nagoya City University. He previously worked at Kyoto University (2002–2009) and Hokkaido University (2009–2023). His research focuses on the foundations of machine learning and data science, as well as their applications in a variety of domains, including quantum information, planetary and earth sciences, and biomedical sciences. He is also interested in human–AI collaboration, ranging from crowdsourcing to agent-participating competitions. He earned his B.S., M.S., and Ph.D. from Kyoto University.

Junichiro Mori is Professor at the Information Technology Center, The University of Tokyo. He received his Ph.D. and M.S. in Information Science and Technology from The University of Tokyo. His research focuses on artificial intelligence (AI), particularly user modelling, information extraction, and social network analysis. He has led several nationally funded projects as principal investigator and was a visiting researcher at the German Research Center for Artificial Intelligence (DFKI) from 2006 to 2008. His recent work spans AI, text and graph mining, and scholarly data analysis.

Hiromi Arai is a Unit Leader of AI Safety and Reliability Unit at RIKEN Center for Advanced Intelligence Project. She received her Ph.D. from Tokyo Institute of Technology. She previously worked as a special postdoctoral researcher at RIKEN, as Assistant Professor at the University of Tokyo, and as Senior Researcher at NICT. Her research focuses on trustworthy AI, including fairness in machine learning, AI security and privacy, and explainable AI.