Can Generative AI improve social science?

被引:8
|
作者
Bail, Christopher A. [1 ,2 ,3 ]
机构
[1] Duke Univ, Dept Sociol, Durham, NC 27708 USA
[2] Duke Univ, Dept Polit Sci, Durham, NC 27708 USA
[3] Duke Univ, Dept Publ Policy, Durham, NC 27708 USA
关键词
Generative AI; social science; agent-based model; survey research; algorithmic bias; BIAS;
D O I
10.1073/pnas.2314021121
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:10
相关论文
共 50 条