Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens
This work addresses bias in AI systems for people with disabilities, highlighting intersectional discrimination, but it is incremental as it builds on existing bias analysis methods.
The study tackled ableist bias in NLP systems by analyzing a BERT model's word predictions, finding statistically significant disadvantages for people with disabilities and exploring overlapping discrimination related to gender and race.
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.