Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals
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Abstract (arXiv)
We investigate whether large language models (LLMs) systematically discriminate in candidate evaluations based on applicant name ethnicity and/or institutional prestige and geographic location. Three factorial experiments are reported (4,320 API calls, four LLMs, five professional domains). Study 1 (3x4 design) finds a statistically robust institution-tier gradient of +0.297 points on a 10-point scale (95% bootstrap CI: +0.175 to +0.422), while name-origin effects are negligible and non-significant (95% CI crosses zero). Study 2 (2x2 Prestige x Country design) breaks the prestige-geography confound: the prestige effect (+0.185; 95% CI: +0.093 to +0.275) exceeds the country-of-origin effect (+0.126; 95% CI: +0.037 to +0.218) by 1.5x. Study 3 (2x2 Journal x Institution design) reveals that journal prestige (Nature vs. a peripheral open-access journal) dominates institutional prestige by 5.7x: journal effect +1.937 (95% CI: +1.811 to +2.062) vs. institution effect +0.341 (95% CI: +0.184 to +0.504). A "rescue effect" is confirmed: publishing in Nature compensates for low institutional prestige more strongly for candidates from the University of Guayaquil (+2.127) than from MIT (+1.745). Results are quantified using the Neutrosophic Bias Index NBI<T,I,F>; the I component reveals elevated evaluation inconsistency for low-prestige profiles, an epistemic disadvantage not captured by mean-only metrics. Code and data: https://github.com/mleyvaz/geo-bias-llm
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References · 13
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
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- 2024Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?cited by 25
- 2025Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Educationcited by 14
- 2024Evaluation of LLMs Biases Towards Elite Universities: A Persona-Based Explorationcited by 7
- 2025Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMscited by 7
- 2025Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Modelscited by 6
- 2025Prestige over merit: An adapted audit of LLM bias in peer reviewcited by 5
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