Rational Social Learning Makes Group Hiring More Efficient and Biased

Humans learn not only from personal experience but also by observing others. Integrating social information often allows groups to make decisions more efficiently and accurately than individuals, yet it can also generate persistent bias. These opposing outcomes are typically attributed to different mechanisms, with optimal outcomes linked to how people rationally update their beliefs, share information, and make decisions, whereas biased outcomes are attributed to frictions in these processes. Here, we show that the same underlying process -- rational social learning -- can produce both effects. Whether social learning improves or degrades group performance depends on the structure of the environment, particularly how clearly options differ. Using hiring decisions as a relevant context, we study networks of Bayesian-rational learners, generative AI agents, human online participants, and hiring professionals making decisions independently or collectively. Our design strips away common biases in social learning by embedding agents in fully connected networks and sharing information in their original form, while causally varying the decision environment and network structure. Across all four cases, integrating social information improves efficiency when one option is objectively optimal. However, when multiple options are equally optimal, the same learning process amplifies early random signals and prematurely reduces exploration, leading to bias. In these cases, biased outcomes do not reflect a failure of rationality, but rather a predictable consequence of rational inference. These results identify a unifying psychological mechanism underlying both collective intelligence and collective bias, with implications for designing fair decision-making systems in human and AI collectives.