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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.15122 |
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Table of Contents:
- Large language models (LLMs) are playing an increasingly integral, though largely informal, role in scholarly peer review. Yet it remains unclear whether LLMs reproduce the biases observed in human decision-making. We adapt a resume-style audit to scientific publishing, developing a multi-role LLM simulation (editor/reviewer) that evaluates a representative set of high-quality manuscripts across the physical, biological, and social sciences under randomized author identities (institutional prestige, gender, race). The audit reveals a strong and consistent institutional-prestige bias: identical papers attributed to low-prestige affiliations face a significantly higher risk of rejection, despite only modest differences in LLM-assessed quality. To probe mechanisms, we generate synthetic CVs for the same author profiles; these encode large prestige-linked disparities and an inverted prestige-tenure gradient relative to national benchmarks. The results suggest that both domain norms and prestige-linked priors embedded in training data shape paper-level outcomes once identity is visible, converting affiliation into a decisive status cue.