A Study of Implicit Bias in Pretrained Language Models against People with Disabilities
Pranav Narayanan Venkit | Mukund Srinath | Shomir Wilson
Proceedings of the 29th International Conference on Computational Linguistics
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.
Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies
Mukund Srinath | Shomir Wilson | C Lee Giles
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)