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Paper short abstract

What are the meanings and implications of AI research focusing on simulating human behaviors and interactions? This paper addresses this question by drawing on ethnographic fieldwork at an academic AI lab and examining the assumptions and practices of researchers involved in such projects.

Paper long abstract

Recent advances in AI development have been driven largely by progress in natural language processing, incentivizing researchers to use AI systems to model and simulate human behavior, preferences, and judgment as a way of demonstrating their abilities and practical usefulness. These efforts often give rise to broad claims about AI performance, invoking notions of reasoning, understanding, and agency. They also expose a familiar tension: the social phenomena being modeled are contested and context-dependent, yet evaluation practices rely on assumptions of ground truth and clear validation. This paper explores this tension by drawing on ethnographic fieldwork in an academic computer science lab in China that focused on legal AI and social simulation. The author examines how AI researchers employ social science data, concepts, and theories to build and evaluate AI systems, while also considering the broader institutional and technical contexts shaping their work. Focusing on the practices of AI researchers provides a starting point for asking how to understand their work at all. What does engagement with social science contribute to AI research projects? To what extent does this reflect collaboration across disciplines, and to what extent is social science engaged more instrumentally to legitimize claims about AI systems’ abilities? The paper also considers what follows from this: what kinds of understandings of social realities may be strengthened or sidelined through the use of AI systems, and how social scientists might respond to these processes.