Data Representativeness in Accessibility Datasets: A Meta-Analysis.

ABSTRACT: 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 paper, 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

SUBMITTER: Kamikubo R 

PROVIDER: S-EPMC10024595 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature