AI for social science and social science of AI: A Survey
Ruoxi Xu Yingfei Sun Mengjie Ren Shiguang Guo Ruotong Pan Hongyu Lin Le Sun Xianpei Han
Recent advancements in artificial intelligence, particularly with the
emergence of large language models (LLMs), have sparked a rethinking of
artificial general intelligence possibilities. The increasing human-like
capabilities of AI are also attracting attention in social science research,
leading to various studies exploring the combination of these two fields. In
this survey, we systematically categorize previous explorations in the
combination of AI and social science into two directions that share common
technical approaches but differ in their research objectives. The first
direction is focused on AI for social science, where AI is utilized as a
powerful tool to enhance various stages of social science research. While the
second direction is the social science of AI, which examines AI agents as
social entities with their human-like cognitive and linguistic capabilities. By
conducting a thorough review, particularly on the substantial progress
facilitated by recent advancements in large language models, this paper
introduces a fresh perspective to reassess the relationship between AI and
social science, provides a cohesive framework that allows researchers to
understand the distinctions and connections between AI for social science and
social science of AI, and also summarized state-of-art experiment simulation
platforms to facilitate research in these two directions. We believe that as AI
technology continues to advance and intelligent agents find increasing
applications in our daily lives, the significance of the combination of AI and
social science will become even more prominent.