Artificial Intelligence Fairness in the Context of Accessibility Research on Intelligent Systems for People who are Deaf or Hard of Hearing

TL;DR

This paper explores AI fairness issues for Deaf and Hard of Hearing users, emphasizing data inclusion, interpretability, ethical responsibilities, evaluation metrics, and the impact of AI on user abilities in accessibility technologies.

Contribution

It highlights the importance of inclusive data, interpretability, and ethical considerations in developing fair AI systems for DHH individuals, advancing accessibility research.

Findings

Need for inclusive datasets from people with disabilities

Importance of interpretability in AI accessibility tools

Development of appropriate evaluation metrics for trustworthiness

Abstract

We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on human-computer interaction (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.