Start Generating: Harnessing Generative AI for Social Scientific Research
Join us as Thomas Davidson, Rutgers University, explores applications to the study of text and images across multiple domains, including computational, qualitative, and experimental research.
This talk is part of the Data Science Program's Ethics In Data Science talk series.
Start Generating: Harnessing Generative AI for Social Scientific Research
How can generative artificial intelligence (GAI) be used for social science research? This paper explores applications to the study of text and images across multiple domains, including computational, qualitative, and experimental research. Drawing upon recent research and stylized experiments with DALL-E and GPT-4, I illustrate the potential applications of text-to-text, image-to-text, and text-to-image models for sociological research. Across these areas, GAI can make advanced computational methods more efficient, flexible, and accessible. The paper also emphasizes several challenges raised by these technologies, including interpretability, transparency, reliability, reproducibility, ethics, and privacy, as well as the implications of bias and bias mitigation efforts and the trade-offs between proprietary models and open-source alternatives. When used with care, these technologies can help advance many different areas of methodology, complementing and enhancing our existing toolkits.
Thomas Davidson is an Assistant Professor in the Department of Sociology at Rutgers University. He received his Ph.D. in Sociology from Cornell University in 2020. His research interests include political sociology, social movements, and the sociology of culture. His research uses digital trace data from social media and other websites combined with statistical analysis and computational methods, including natural language processing and machine learning.
He is currently working on several projects on populism, far-right politics, and social media. He is also working on research understanding the evolution of hate speech moderation policies on social media platforms and developing experimental research to understand how perceptions of hate speech vary according to social contexts.
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