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Much of the world's population experiences some form of disability duringtheir lifetime. Caution must be exercised while designing natural languageprocessing (NLP) systems to prevent systems from inadvertently perpetuatingableist bias against people with disabilities, i.e., prejudice that favorsthose with typical abilities. We report on various analyses based on wordpredictions of a large-scale BERT language model. Statistically significantresults demonstrate that people with disabilities can be disadvantaged.Findings also explore overlapping forms of discrimination related tointerconnected gender and race identities.

Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens