Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring
Employers are increasingly using large language models (LLMs) to automate their hiring process. This paper investigates the risk of monocultural biases, in which the widespread deployment of large language models homogenizes biases across the labor market, leading to greater systemic exclusion for certain demographic groups. For ten LLMs, we measure hiring biases across their base and post-trained versions to identify which stage, pre-training or post-training, lead to monocultural biases. We find that, compared to their base models, post-trained models are 3.6% less likely to callback older applicants. This negative shift occurs in eight of the ten models that we evaluate. Post-trained m...
| Authors | Matthew Bone, Fabian Stephany, Maria del Rio-Chanona |
|---|---|
| Primary category | cs.CL |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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