Known to Whom? Auditing Cultural Epistemic Prejudice in Large Language Models

As large language models (LLMs) become increasingly central to how people search for and consume information, biases in their outputs risk becoming embedded in users’ knowledge-seeking practices. Recent work has documented biases in the knowledge LLMs represent and generate, but how they infer what users are likely to know remains largely unexplored. Here, we provide a large-scale analysis of one way these inferences can disadvantage users: epistemic prejudice, or LLMs’ tendency to systematically misjudge how familiar users are with entities from different cultural contexts. We develop a controlled disambiguation task in which LLMs have to choose which of two entities—one US and one non-US—a user is unfamiliar with, and test them with different cues about the user’s cultural background: English prompts, native-language (e.g., Hindi) prompts, and explicitly stated user location (e.g., India). Across seven LLMs, ten languages, and five domains, models show a strong tendency to predict that the user knows the US entity and is unfamiliar with the non-US entity. Native language and explicit location alter this tendency in some settings, but the preference strongly persists across many languages and domains. For example, for users asking in Hindi, LLMs predominantly assume that they are more familiar with US entities than with Indian entities, suggesting the presence of strong epistemic prejudice in those models. In further experiments, we find that epistemic prejudice cannot be easily reduced via debiasing instructions. Finally, we analyze LLM training data and find that, relative to US entities, non-US entities are far more underrepresented than real-world visibility would suggest. Together, these findings establish epistemic prejudice as a distinct and understudied form of bias in LLMs: not a bias in what models know, but in what models assume their users know.