Sharing Practices for Datasets Related to Accessibility and Aging
This work tackles the problem of scarce datasets for accessibility and aging, which hinders innovation and bias mitigation in AI applications for people with disabilities and older adults, by providing a foundational analysis and calling for improved frameworks.
The researchers conducted a systematic review of 137 accessibility datasets over 35 years to address their scarcity, uncovering patterns in data collection and sharing practices and highlighting tensions between benefits and risks.
Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.