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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1324-1332 |
| Number of pages | 9 |
| Journal | Proceedings - International Conference on Computational Linguistics, COLING |
| Volume | 29 |
| Issue number | 1 |
| State | Published - 2022 |
| Event | 29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of Duration: Oct 12 2022 → Oct 17 2022 |
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Computer Science Applications
- Theoretical Computer Science