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

Saad Hassan; Matt Huenerfauth; Cecilia Ovesdotter Alm

EMNLP 2021finding43 citations

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

Saad Hassan, Matt Huenerfauth, Cecilia Ovesdotter Alm

Abstract

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.

BibTeX

@inproceedings{hassan-etal-2021-unpacking-interdependent,
    title = "Unpacking the Interdependent Systems of Discrimination: Ableist Bias in {NLP} Systems through an Intersectional Lens",
    author = "Hassan, Saad  and
      Huenerfauth, Matt  and
      Alm, Cecilia Ovesdotter",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.267/",
    doi = "10.18653/v1/2021.findings-emnlp.267",
    pages = "3116--3123"
}