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Implications of AI for Qualitative, Quantitative, and Computational Sociological Research
Mon, August 10, 8:00 to 9:30am, TBASession Submission Type: Paper Session (90 minute)
Description
AI models and tools introduce new opportunities and challenges for sociological research, with diverse potential applications and implications for qualitative, quantitative, and computational sociologists. The goals of this session are to highlight how sociologists are engaging with AI across all methods and to engage in a collective conversation about the implications of AI for sociological methods. This session seeks qualitative, quantitative, and/or computational research that applies AI in novel ways. Submissions that can facilitate cross-method dialogue about best practices, lessons learned, norms, and ethics are especially welcome.
Sub Unit
Presider
Individual Presentations
Banning the Bots: Survey data collection in the era of generative AI - Laura DeMarco, North Carolina State University; Alexis Jones, North Carolina State University; Sadé Lindsay, Cornell University
Learning About Black Life Through Memorials: The Funeral Programs Project - Trevon D. Logan, Ohio State University
Open Qualitative Inquiry and AI: The Case(s) for Deidentification - Corey M. Abramson, Rice University; Rongchen Wang, Rice University; Jarmin Yeh, University of California, San Francisco
A Methodological Framework for AI-Assisted Image Stimulus Design - Han Zhang, Brown University; Yishi Yin, Brown University
The Propriety of Generative AI in Sociological Research - Dustin S. Stoltz, Lehigh University; AJ Alvero, Cornell University; Oscar Stuhler, Northwestern University; Marshall A. Taylor, New Mexico State University