Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game

Gender and race inferred from an individual's name are a notable source of stereotypes and biases that subtly influence social interactions. Abundant evidence from human experiments has revealed the preferential treatment that one receives when one's name suggests a predominant gender or race. As LLMs acquire more capabilities and begin to support everyday applications, it becomes crucial to examine whether they manifest similar biases when encountering names in a complex social interaction. In contrast to previous work that studies name-based biases in language models at a more fundamental level, such as word representations, we challenge three prominent models to predict the outcome of a modified Trust Game, a well-publicized paradigm for studying trust and reciprocity. To ensure the internal validity of our experiments, we have carefully curated a list of racially representative surnames to identify players in a Trust Game and rigorously verified the construct validity of our prompts. The results of our experiments show that our approach can detect name-based biases in both base and instruction-tuned models.