@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",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens
%A Hassan, Saad
%A Huenerfauth, Matt
%A Alm, Cecilia Ovesdotter
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F hassan-etal-2021-unpacking-interdependent
%X 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.
%R 10.18653/v1/2021.findings-emnlp.267
%U https://aclanthology.org/2021.findings-emnlp.267/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.267
%P 3116-3123
Markdown (Informal)
[Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens](https://aclanthology.org/2021.findings-emnlp.267/) (Hassan et al., Findings 2021)
ACL