@inproceedings{0451b6c6e0894e9992d5a8e5e8813aba,
title = "Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models",
abstract = "We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings. We then create the Bias Identification Test in Sentiment (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models. Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT. Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.",
author = "Venkit, {Pranav Narayanan} and Mukund Srinath and Shomir Wilson",
note = "Publisher Copyright: {\textcopyright} 2023 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All rights reserved.; 3rd Workshop on Trustworthy Natural Language Processing, TrustNLP 2023, co-located with ACL 2023 ; Conference date: 14-07-2023",
year = "2023",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "26--34",
editor = "Anaelia Ovalle and Kai-Wei Chang and Kai-Wei Chang and Ninareh Mehrabi and Yada Pruksachatkun and Aram Galystan and Aram Galystan and Jwala Dhamala and Apurv Verma and Trista Cao and Anoop Kumar and Rahul Gupta",
booktitle = "3rd Workshop on Trustworthy Natural Language Processing, TrustNLP 2023 - Proceedings of the Workshop",
}