Abstract
Pretrained language models (PLMs) have been shown to exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biases in language technologies. However, PLMs’ biases against people with disabilities (PWDs) have received little attention, in spite of their potential to cause similar harms. Using perturbation sensitivity analysis, we test an assortment of popular word embedding-based and transformer-based PLMs and show significant biases against PWDs in all of them. The results demonstrate how models trained on large corpora widely favor ableist language.- Anthology ID:
- 2022.coling-1.113
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1324–1332
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.113
- DOI:
- Bibkey:
- Cite (ACL):
- Pranav Narayanan Venkit, Mukund Srinath, and Shomir Wilson. 2022. A Study of Implicit Bias in Pretrained Language Models against People with Disabilities. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1324–1332, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- A Study of Implicit Bias in Pretrained Language Models against People with Disabilities (Venkit et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.113.pdf
Export citation
@inproceedings{venkit-etal-2022-study,
title = "A Study of Implicit Bias in Pretrained Language Models against People with Disabilities",
author = "Venkit, Pranav Narayanan and
Srinath, Mukund and
Wilson, Shomir",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.113",
pages = "1324--1332",
abstract = "Pretrained language models (PLMs) have been shown to exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biases in language technologies. However, PLMs{'} biases against people with disabilities (PWDs) have received little attention, in spite of their potential to cause similar harms. Using perturbation sensitivity analysis, we test an assortment of popular word embedding-based and transformer-based PLMs and show significant biases against PWDs in all of them. The results demonstrate how models trained on large corpora widely favor ableist language.",
}
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<abstract>Pretrained language models (PLMs) have been shown to exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biases in language technologies. However, PLMs’ biases against people with disabilities (PWDs) have received little attention, in spite of their potential to cause similar harms. Using perturbation sensitivity analysis, we test an assortment of popular word embedding-based and transformer-based PLMs and show significant biases against PWDs in all of them. The results demonstrate how models trained on large corpora widely favor ableist language.</abstract>
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%0 Conference Proceedings %T A Study of Implicit Bias in Pretrained Language Models against People with Disabilities %A Venkit, Pranav Narayanan %A Srinath, Mukund %A Wilson, Shomir %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F venkit-etal-2022-study %X Pretrained language models (PLMs) have been shown to exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biases in language technologies. However, PLMs’ biases against people with disabilities (PWDs) have received little attention, in spite of their potential to cause similar harms. Using perturbation sensitivity analysis, we test an assortment of popular word embedding-based and transformer-based PLMs and show significant biases against PWDs in all of them. The results demonstrate how models trained on large corpora widely favor ableist language. %U https://aclanthology.org/2022.coling-1.113 %P 1324-1332
Markdown (Informal)
[A Study of Implicit Bias in Pretrained Language Models against People with Disabilities](https://aclanthology.org/2022.coling-1.113) (Venkit et al., COLING 2022)
- A Study of Implicit Bias in Pretrained Language Models against People with Disabilities (Venkit et al., COLING 2022)
ACL
- Pranav Narayanan Venkit, Mukund Srinath, and Shomir Wilson. 2022. A Study of Implicit Bias in Pretrained Language Models against People with Disabilities. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1324–1332, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.