Bias Against 93 Stigmatized Groups in Masked Language Models and
  Downstream Sentiment Classification Tasks
Katelyn X. Mei Sonia Fereidooni Aylin Caliskan
The rapid deployment of artificial intelligence (AI) models demands a
thorough investigation of biases and risks inherent in these models to
understand their impact on individuals and society. This study extends the
focus of bias evaluation in extant work by examining bias against social
stigmas on a large scale. It focuses on 93 stigmatized groups in the United
States, including a wide range of conditions related to disease, disability,
drug use, mental illness, religion, sexuality, socioeconomic status, and other
relevant factors. We investigate bias against these groups in English
pre-trained Masked Language Models (MLMs) and their downstream sentiment
classification tasks. To evaluate the presence of bias against 93 stigmatized
conditions, we identify 29 non-stigmatized conditions to conduct a comparative
analysis. Building upon a psychology scale of social rejection, the Social
Distance Scale, we prompt six MLMs: RoBERTa-base, RoBERTa-large, XLNet-large,
BERTweet-base, BERTweet-large, and DistilBERT. We use human annotations to
analyze the predicted words from these models, with which we measure the extent
of bias against stigmatized groups. When prompts include stigmatized
conditions, the probability of MLMs predicting negative words is approximately
20 percent higher than when prompts have non-stigmatized conditions. In the
sentiment classification tasks, when sentences include stigmatized conditions
related to diseases, disability, education, and mental illness, they are more
likely to be classified as negative. We also observe a strong correlation
between bias in MLMs and their downstream sentiment classifiers (r =0.79). The
evidence indicates that MLMs and their downstream sentiment classification
tasks exhibit biases against socially stigmatized groups.