Can Generative AI improve social science?
By Christopher A Bail. Proceedings of the National Academy of Sciences of the United States of America, 2024
386 citations recorded by the source corpus.
Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative AI. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.
Subfields: General Social Sciences, Safety Research, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/9e0e253a7f71f6702f40b083080232989c482464
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Citations in the corpus
- A survey on large language model based autonomous agents (2023, neutral): “Meanwhile, in [114], LLM-based agents serve as a writing assistant, demonstrating their capability to identify novel research inquiries for social scientists, thereby opening new avenues for exploration and innovation in the field.”
- GPT is an effective tool for multilingual psychological text analysis (2024, background): “Researchers have also recently noted GPT’s ability to help with computational social science tasks (49–54), detect misinformation (55), infer politicians’ ideologies (56), write persuasive political arguments (57), respond to patient questions (58), simulate human research participants (59–61), and…”
- Multi-Agent Collaboration Mechanisms: A Survey of LLMs (2025, neutral): “Studies such as [3, 11] argue the potential of LLMs to enhance traditional social science methods, including survey research, online experiments, automated content analyses, and agent-based modeling.”