Panelists:
1. Louis Tay, Purdue University
About the Panelist:
Louis Tay is William C. Byham Professor in Industrial-Organizational Psychology and has research interests in well-being, data science, measurement, and AI. He has served as an Associate Editor at Organizational Research Methods.
Presentation Overview:
I will be sharing some resources, principles, and ideas around AI use—both in using it for research as an author and as a reviewer.
2. Gargi Sawhney, Auburn University
About the Panelist:
Gargi Sawhney is an Associate Professor of Industrial-Organizational Psychology at Auburn University and Chief Researcher at ResponsePie. Her research examines occupational stress, employee well-being, research methods, and data quality in online research. For more than three years, she has studied survey fraud, including developing her own AI agents that autonomously complete surveys and creating and evaluating tools designed to identify AI-assisted, automated, and fraudulent responses. Her research has appeared in journals including the Journal of Vocational Behavior, Human Resource Management, and the Journal of Occupational and Organizational Psychology, among others. She also serves on the editorial boards of Stress and Health and the International Journal of Stress Management.
Presentation Overview:
AI bots and agents pose a growing threat to the integrity of online survey research. This session presents a practical end-to-end approach for minimizing these risks and protecting data quality. It offers guidance to help authors strengthen their research practices and reviewers assess whether appropriate safeguards have been used.
3. Simone Zhang, New York University
About the Panelist:
Simone Zhang is an Assistant Professor of Sociology at New York University. Her work examines the implications of predictive algorithms and artificial intelligence for social policy, law, and social science research.
Presentation Overview:
In this presentation, I will share research on AI usage among online survey participants. I will also provide suggestions for researchers on how to balance a desire to deter AI usage against the potential inadvertent consequences that anti-AI countermeasures can introduce.