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Social Biases in NLP Models as Barriers for Persons with Disabilities

Stephen Craig Denuyl
Proceedings of ACL 2020, ACL (to appear)
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Building equitable and inclusive technologies demands paying attention to how social attitudes towards persons with disabilities are represented within technology. Representations perpetuated by NLP models often inadvertently encode undesirable social biases from the data on which they are trained. In this paper, first we present evidence of such undesirable biases towards mentions of disability in two different NLP models: toxicity prediction and sentiment analysis. Next, we demonstrate that neural embeddings that are critical first steps in most NLP pipelines also contain undesirable biases towards mentions of disabilities. We then expose the topical biases in the social discourse about some disabilities which may explain such biases in the models; for instance, terms related to gun violence, homelessness, and drug addiction are over-represented in discussions about mental illness.