Communication Dans Un Congrès Année : 2025

"Women do not have heart attacks!" Gender Biases in Automatically Generated Clinical Cases in French

Résumé

Healthcare professionals increasingly include Language Models (LMs) in clinical practice. However, LMs have been shown to exhibit and amplify stereotypical biases that can cause lifethreatening harm in a medical context. This study aims to evaluate gender biases in automatically generated clinical cases in French, on ten disorders. Using seven LMs fine-tuned for clinical case generation and an automatic linguistic gender detection tool, we measure the associations between disorders and gender. We unveil that LMs over-generate cases describing male patients, creating synthetic corpora that are not consistent with documented prevalence for these disorders. For instance, when prompts do not specify a gender, LMs generate eight times more clinical cases describing male (vs. female patients) for heart attack. We discuss the ideal synthetic clinical case corpus and establish that explicitly mentioning demographic information in generation instructions appears to be the fairest strategy. In conclusion, we argue that the presence of gender biases in synthetic text raises concerns about LM-induced harm, especially for women and transgender people.

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Dates et versions

hal-04938811 , version 1 (10-02-2025)

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  • HAL Id : hal-04938811 , version 1

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Fanny Ducel, Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol. "Women do not have heart attacks!" Gender Biases in Automatically Generated Clinical Cases in French. Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, Apr 2025, Albuquerque, United States. ⟨hal-04938811⟩
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