Automated Ableism: An Exploration of Explicit Disability Biases in
  Sentiment and Toxicity Analysis Models
Pranav Narayanan Venkit Mukund Srinath Shomir Wilson
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 \textit{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.