Overview

Fraudulent use of AI is increasingly prevalent in human-subjects research, rendering many traditional bot-detection methods ineffective and, consequently, raising significant concerns about data integrity among researchers who work with human-subjects data. Tools are being developed to address concerns about fraudulent participation; however, it remains unclear which best practices to follow for prevention or mitigation, how effective the adopted preventative measures are, and to what extent one can trust the collected data.

In this one-day in-person workshop, we will bring together human-computer interaction and social computing researchers to exchange strategies for addressing fraudulent participation, assess the effectiveness of existing detection methods, discuss the potential risks and opportunities of AI in human-centered research, and develop method-specific guidelines for fraud prevention/detection and the research dissemination/review process.

The workshop involves full-group activities, breakout discussions divided by research methods, and the production of a draft document summarizing method-specific guidelines that will be shared with the wider CSCW community for feedback. We hope that the conversation started during this workshop will spur deeper engagement from CSCW and the wider SIGCHI research community to implement approaches to minimize the negative impact of AI-enabled fraudulent participation in research.

Call for Participation

Please fill out this Google Form to express your interest in participating the workshop: TBA

The form will be closed on July 31, 2026.

Agenda

Organizers

• Weijia He is an Assistant Professor in the School of Electronics and Computer Science at the University of Southampton. Her research focuses on human-centered security and privacy in ubiquitous computing and emerging technologies.
• EunJeong Cheon is an Assistant Professor at Syracuse University’s School of Information Studies. Her research examines how emerging technologies reshape labor and social relations through ethnographic and critical design methods across international contexts.
• Christina Chung is an Associate Professor in the Department of Computational Media at the University of California, Santa Cruz. She studies personal informatics in various health contexts.
• Dipto Das is a postdoctoral fellow in the Department of Computer Science at the University of Toronto. His mixed-methods, interdisciplinary, community-engaged research approach examines how AI systems and digital platforms shape social relations, discourse, and inequality across various domains and diverse sociopolitical and cultural contexts.
• Harmanpreet Kaur is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, where she co-leads the GroupLens research lab. She studies appropriate reliance on AI in knowledge work (e.g., data science, search) and everyday decision-making settings.
• Toby Jia-Jun Li is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he leads the SaNDwich Lab. He works at the intersection of HCI, ML, and NLP applications, where he uses human-centered methods to design, build, and study interactive systems to empower individuals to create, configure, and extend AI-powered computing systems.
• Jessica Vitak is a Professor in the College of Information at the University of Maryland and a mixed-methods researcher with expertise in surveys, interviews, and focus groups.
• Yaxing Yao is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. His research focuses on human-centered privacy and security, with an emphasis on different populations, such as minors, older adults, and people with disabilities.