This paper examines biases in large language models (LLMs) when generating synthetic populations from responses to personality questionnaires. Using five LLMs, we first assess the representativeness and potential biases in the sociodemographic attributes of the generated personas, as well as their alignment with the intended personality traits. While LLMs successfully reproduce known correlations between personality and sociodemographic variables, all models exhibit pronounced WEIRD (western, educated, industrialized, rich and democratic) biases, favoring young, educated, white, heterosexual, Western individuals with centrist or progressive political views and secular or Christian beliefs. In a second analysis, we manipulate input traits to maximize Neuroticism and Psychoticism scores. Notably, when Psychoticism is maximized, several models produce an overrepresentation of non-binary and LGBTQ+ identities, raising concerns about stereotyping and the potential pathologization of marginalized groups. Our findings highlight both the potential and the risks of using LLMs to generate psychologically grounded synthetic populations.

翻译：本文研究了大语言模型（LLMs）根据人格问卷回答生成合成人群时存在的偏见。我们使用五种LLM，首先评估了生成角色在社会人口属性上的代表性及潜在偏见，以及它们与目标人格特质的匹配程度。虽然LLMs成功复现了人格与社会人口变量之间的已知相关性，但所有模型均表现出显著的WEIRD（西方、受过教育、工业化、富裕和民主）偏见，倾向于生成年轻、受过教育、白人、异性恋、具有中立或进步政治观点以及世俗或基督教信仰的西方个体。在第二项分析中，我们通过操纵输入特征来最大化神经质和精神质得分。值得注意的是，当精神质最大化时，多个模型生成的合成人群中非二元性别和LGBTQ+身份的比例过高，这引发了关于对边缘群体的刻板印象和潜在病理化风险的担忧。我们的研究结果凸显了使用LLMs生成基于心理学的合成人群的潜力与风险。