  Much of the world's population experiences some form of disability during
their lifetime. Caution must be exercised while designing natural language
processing (NLP) systems to prevent systems from inadvertently perpetuating
ableist bias against people with disabilities, i.e., prejudice that favors
those with typical abilities. We report on various analyses based on word
predictions of a large-scale BERT language model. Statistically significant
results demonstrate that people with disabilities can be disadvantaged.
Findings also explore overlapping forms of discrimination related to
interconnected gender and race identities.
