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arXiv · 2208.01341

Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories

Gizem Sogancioglu·Fabian Mijsters·Amar van Uden·Jelle Peperzak
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Abstract

Clinical word embeddings are extensively used in various Bio-NLP problems as a state-of-the-art feature vector representation. Although they are quite successful at the semantic representation of words, due to the dataset - which potentially carries statistical and societal bias - on which they are trained, they might exhibit gender stereotypes. This study analyses gender bias of clinical embeddings on three medical categories: mental disorders, sexually transmitted diseases, and personality traits. To this extent, we analyze two different pre-trained embeddings namely (contextualized) clinical-BERT and (non-contextualized) BioWordVec. We show that both embeddings are biased towards sensitive gender groups but BioWordVec exhibits a higher bias than clinical-BERT for all three categories. Moreover, our analyses show that clinical embeddings carry a high degree of bias for some medical terms and diseases which is conflicting with medical literature. Having such an ill-founded relationship might cause harm in downstream applications that use clinical embeddings.

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Gizem Sogancioglu, Fabian Mijsters, Amar van Uden, Jelle Peperzak. 2022-08-02. Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories. https://arxiv.org/abs/2208.01341

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