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.

翻译：我们分析了情感分析和毒性检测模型，以检测它们对残障人士(PWD)是否存在显性偏见。采用扰动敏感性分析的偏见识别框架，我们考察了社交媒体平台（特别是推特和Reddit）上与PWD相关的对话，以洞察残疾偏见如何在真实社交环境中传播。随后，我们创建了情感偏见识别测试(BITS)语料库，用于量化任何情感分析和毒性检测模型中的显性残疾偏见。本研究利用BITS揭示了四种开放AI即服务(AIaaS)情感分析工具——TextBlob、VADER、Google Cloud Natural Language API、DistilBERT，以及两个毒性检测模型——两个版本的Toxic-BERT中存在显著偏见。研究结果表明，所有这些模型均对PWD表现出统计上显著的显性偏见。