Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health
Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training data, which is potentially impactful in sensitive domains like healthcare. While existing benchmarks evaluate biases related to individual social determinants of health (SDoH) such as gender or ethnicity, they often overlook interactions between these factors and lack context-specific assessments. This study investigates bias in LLMs by probing the relationships between gender and other SDoH in French patient records. Through a series of experiments, we found that embedded stereotypes can be probed using SDoH input and that LLMs rely on embedded stereot...
| Authors | Trung Hieu Ngo, Adrien Bazoge, Solen Quiniou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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