  Word embeddings are extensively used in various NLP problems as a
state-of-the-art semantic feature vector representation. Despite their success
on various tasks and domains, they might exhibit an undesired bias for
stereotypical categories due to statistical and societal biases that exist in
the dataset they are trained on. In this study, we analyze the gender bias in
four different pre-trained word embeddings specifically for the depression
category in the mental disorder domain. We use contextual and non-contextual
embeddings that are trained on domain-independent as well as clinical
domain-specific data. We observe that embeddings carry bias for depression
towards different gender groups depending on the type of embeddings. Moreover,
we demonstrate that these undesired correlations are transferred to the
downstream task for depression phenotype recognition. We find that data
augmentation by simply swapping gender words mitigates the bias significantly
in the downstream task.

Computation and Language;Machine Learning