ArXiv Preprint
Mental health stigma prevents many individuals from receiving the appropriate
care, and social psychology studies have shown that mental health tends to be
overlooked in men. In this work, we investigate gendered mental health stigma
in masked language models. In doing so, we operationalize mental health stigma
by developing a framework grounded in psychology research: we use clinical
psychology literature to curate prompts, then evaluate the models' propensity
to generate gendered words. We find that masked language models capture
societal stigma about gender in mental health: models are consistently more
likely to predict female subjects than male in sentences about having a mental
health condition (32% vs. 19%), and this disparity is exacerbated for sentences
that indicate treatment-seeking behavior. Furthermore, we find that different
models capture dimensions of stigma differently for men and women, associating
stereotypes like anger, blame, and pity more with women with mental health
conditions than with men. In showing the complex nuances of models' gendered
mental health stigma, we demonstrate that context and overlapping dimensions of
identity are important considerations when assessing computational models'
social biases.
Inna Wanyin Lin, Lucille Njoo, Anjalie Field, Ashish Sharma, Katharina Reinecke, Tim Althoff, Yulia Tsvetkov
2022-10-27
