发表机构
University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过大规模受控实验发现，大语言模型在信贷、招聘和租房评估中对无学位白人男性的评分最低，揭示了AI偏见可能掩盖特定群体的劣势。
AI 中文摘要
在受控的大语言模型评估中，针对信贷、招聘和租房申请，无大学学位的白人男性在八个性别-种族-教育群体中获得的平均评分最低。我们与12个开发者团队的18个模型进行了全因子情景实验，在保持每个情景中陈述的财务或职业状况不变的情况下，改变性别、种族、年龄、公民身份和教育程度。每个模型在每个情景中对所有32个档案各评估十次，共产生17,280个评分。对模型、年龄和公民身份取平均后，无学位的白人男性在八个群体中评分最低，在0-100分制下，信贷评分为75.87，招聘评分为92.71，租房评分为86.62。有学位的黑人女性获得最高平均评分，相应的差距分别为2.94、3.66和3.56分。在三个情景中，单独的属性效应均有利于女性、黑人申请者和有学位者。在54个模型-情景组合中，无学位的白人男性在46个组合（85.2%）中平均分最低或第二低。这一模式与无学位白人男性经济和健康脆弱性日益增长的证据相关联，凸显了该群体的劣势可能被广泛的种族或性别类别所掩盖。
英文摘要
White men without an undergraduate degree receive the lowest average ratings among eight gender-race-education groups in controlled large-language-model evaluations of credit, hiring, and rental applications. We conduct full-factorial vignette experiments with 18 models from 12 developer groups, varying gender, race, age, citizenship, and education while holding stated financial or occupational circumstances constant within each setting. Each model evaluates all 32 profiles ten times per setting, yielding 17,280 ratings. Averaging over models, age, and citizenship, ratings for White men without degrees are the lowest among the eight groups, at 75.87 in credit, 92.71 in hiring, and 86.62 in rental housing on a 0-100 scale. Black women with degrees receive the highest average ratings, with corresponding gaps of 2.94, 3.66, and 3.56 points. Separate attribute effects favor women, Black applicants, and degree holders in all three settings. White men without degrees have the lowest or second-lowest mean in 46 of 54 model-scenario combinations (85.2%). This pattern connects to evidence of growing economic and health vulnerabilities among White men without degrees, highlighting a group whose disadvantages can be obscured by broad racial or gender categories.
