Sharing Practices for Datasets Related to Accessibility and Aging

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.

arXiv commentPreprint, The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2021)

References in corpus (6)

• Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
• Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation
• AI and Accessibility: A Discussion of Ethical Considerations
• Computer keyboard interaction as an indicator of early Parkinson's disease
• Predicting risk of dyslexia with an online gamified test
• Privacy, ethics, and data access: A case study of the Fragile Families Challenge

Cited by in corpus (4)

• Data Representativeness in Accessibility Datasets: A Meta-Analysis
• Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People
• AccessShare: Co-designing Data Access and Sharing with Blind People
• Evaluating the Social Impact of Generative AI Systems in Systems and Society