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 call these accessibility datasets. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.

本文研究AI公平性和包容性中的困境，并探讨了使用accessibility datasets对有潜力缓解AI偏见的不同人群的代表性。我们发现，accessibility datasets代表不同年龄段的人，但存在性别和种族代表性差距。同时，我们还研究了人口统计变量的敏感性和复杂性如何使分类变得困难和不一致，希望我们的工作能够扩大边缘社区在AI-infused系统中的代表性。

辅助性数据集中的数据代表性：一项元分析