Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research

Abstract

The development of Generative Artificial Intelligence (GAI), exemplified by the introduction of ChatGPT, has sparked significant public debate. This paper explores the applications of these technologies as methods for sociological research, examining potential applications across three areas of methodology: computational, experimental, and qualitative research. Drawing upon recent research and stylized experiments with DALL-E and GPT-4, the paper argues that these technologies make advanced computational methods more accessible to the discipline and illustrates the generative potential of text-to-text, text-to-image, and image-to-text models. The paper also examines several new challenges raised by GAI, including interpretability, transparency, reliability, reproducibility, ethics, and privacy, as well as the implications for bias and bias mitigation efforts. The trade-offs between closed-source, proprietary models and open-source alternatives are also considered in the context of these challenges. Overall, this paper encourages sociologists to exercise caution with these models while embracing the opportunities presented by these technologies.