Data Representativeness in Accessibility Datasets

Summary:  View help for Summary As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this project, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets–datasets sourced from people with disabilities and older adults–that can potentially play an important role in mitigating bias for inclusive AI-infused applications. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. The data codes for these datasets annotated with demographic metadata are available to the research community.

Funding Sources:  View help for Funding Sources United States Department of Health and Human Services. Administration for Community Living. National Institute on Disability, Independent Living, and Rehabilitation Research