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To enable more websites to provide content in the form of sign language, we investigate software to partially automate the synthesis of animations of American Sign Language (ASL), based on a human-authored message specification. We automatically select: where prosodic pauses should be inserted (based on the syntax or other features), the timeduration of these pauses, and the variations of the speed at which individual words are performed (e.g. slower at the end of phrases). Based on an analysis of a corpus of multisentence ASL recordings with motion-capture data, we trained machine-learning models, which were evaluated in a cross-validation study. The best model out-performed a prior state-of-the-art ASL timing model. In a study with native ASL signers evaluating animations generated from either our new model or from a simple baseline (uniform speed and no pauses), participants indicated a preference for speed and pausing in ASL animations from our model.

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We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on HCI for AI-based systems for people who are Deaf or Hard of Hearing (DHH). In particular, we discuss the need for inclusion of data from people with disabilities in training sets, the lack of interpretability of AI systems, ethical responsibilities of access technology researchers and companies, the need for appropriate evaluation metrics for AI-based access technologies (to determine if they are ready to be deployed and if they can be trusted by users), and the ways in which AI systems influence human behavior and influence the set of abilities needed by users to successfully interact with computing systems.