arXiv AI August 20, 2026 By Maikel Leyva-Vazquez, Florentin Smarandache

Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals

The study examines whether large language models (LLMs) discriminate against candidates based on institutional prestige and geographic location. Three factorial experiments (4,320 API calls across four LLMs and five domains) reveal a significant institution‑tier gradient (+0.297 points on a 10‑point scale), a stronger prestige effect than country‑of‑origin effect, and a dominant journal prestige effect (Nature vs. a peripheral journal). The authors also identify a ‘rescue effect’ where publishing in Nature mitigates low institutional prestige, and quantify bias using the Neutrosophic Bias Index, highlighting evaluation inconsistency for low‑prestige profiles.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

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arXiv AI

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By Yuan Yuan

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arXiv AI

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By Siddharth Vohra, Manikandan Ravikiran

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arXiv AI

Aug 13

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By Petter T\"ornberg

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arXiv Computation and Language

Sep 22

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By Matthew Bone, Fabian Stephany, Maria del Rio-Chanona

llms

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