Sharing Practices for Datasets Related to Accessibility and Aging

TL;DR

This paper systematically reviews 137 accessibility datasets from various disciplines over 35 years, analyzing sharing practices, challenges, and opportunities to improve data availability for assistive and inclusive AI applications.

Contribution

It provides a comprehensive analysis of data collection, sharing practices, and community-specific challenges in accessibility datasets, highlighting the need for tailored privacy frameworks.

Findings

Patterns in data collection and sharing practices identified

Tensions between benefits and risks in data sharing analyzed

Recommendations for privacy frameworks tailored to accessibility data

Abstract

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.