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• Unintended Machine Learning Biases as Social Barriers for Persons with Disabilities

  Ben Hutchinson, Vinodkumar Prabhakaran, Remi Denton, Kellie Webster, Yu Zhong, Stephen Denuyl · 2020 · SIGACCESS Accessibility and Computing

  This paper from Google's Ethical AI team provides concrete empirical evidence that widely deployed NLP models encode measurable biases against people with disabilities, creating social barriers through technology. The authors examine three layers of bias using a perturbation…

  machine learning bias · NLP · toxicity detection · sentiment analysis · disability representation

• Rescribe: Authoring and Automatically Editing Audio Descriptions

  Amy Pavel, Gabriel Reyes, Jeffrey P. Bigham · 2020 · Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST '20)

  This paper introduces Rescribe, a tool that helps authors create and refine audio descriptions for videos. Audio descriptions make video content accessible to blind and visually impaired viewers by narrating important visual information during gaps in the existing audio track. A…

  audio description · video accessibility · blind and low vision · NLP · sentence compression

• Predicting the Understandability of Imperfect English Captions for People Who Are Deaf or Hard of Hearing

  Sushant Kafle, Matt Huenerfauth · 2019 · ACM Transactions on Accessible Computing (TACCESS)

  This paper tackles a fundamental measurement problem in ASR-based captioning for Deaf and Hard-of-Hearing (DHH) users: the standard Word Error Rate (WER) metric has little correlation with how DHH users actually perceive caption quality. WER treats all word errors as equally…

  automatic speech recognition · captioning · deaf and hard of hearing · evaluation metrics · word error rate

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