  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 large
language models 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.
