Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals
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Computer Science > Computation and Language
arXiv:2608.18107 (cs)
[Submitted on 10 Jun 2026]
Title:Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals
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Abstract: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: this https URL
| Comments: | 11 pages, 3 figures. Extended English version of an earlier two-study Spanish-language paper published in Neutrosophic Computing and Machine Learning (2026); this version adds Study 3 (journal x institution prestige) and bootstrap confidence intervals throughout |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| MSC classes: | 68T50, 62F40, 03B52 |
| ACM classes: | I.2.7; K.4.2 |
| Cite as: | arXiv:2608.18107 [cs.CL] |
| (or arXiv:2608.18107v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18107
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals, by Maikel Leyva-Vazquez and Florentin Smarandache
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