可访问性数据集的数据代表性：一项Meta分析
Data Representativeness in Accessibility Datasets: A Meta-Analysis
摘要：
随着数据驱动系统越来越大规模地部署，人们对训练数据中代表性不足的历史上被边缘化的群体的不公平和歧视性结果产生了道德关切。作为回应，围绕人工智能公平性和包容性的工作需要代表各种人口群体的数据集。在本文中，我们分析了可访问性数据集（来自残疾人和老年人的数据集）中年龄、性别、种族和民族的代表性，这些数据集可能在减轻包容性人工智能应用程序的偏见方面发挥重要作用。我们通过审查190个数据集的公开信息来检查残疾人数据集中的当前表示状态，我们称这些数据集为可访问性数据集。我们发现，可访问性数据集代表不同的年龄，但有性别和种族的代表差距。此外，我们调查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（例如，性别、种族和民族），标签的来源往往未知。通过反思残疾人数据贡献者代表性的当前挑战和机遇，我们希望我们的努力扩大了将边缘化社区更多地纳入人工智能系统的可能性空间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
