As research and industry moves towards large-scale models capable of numerous
downstream tasks, the complexity of understanding multi-modal datasets that
give nuance to models rapidly increases. A clear and thorough understanding of
a dataset's origins, development, intent, ethical considerations and evolution
becomes a necessary step for the responsible and informed deployment of models,
especially those in people-facing contexts and high-risk domains. However, the
burden of this understanding often falls on the intelligibility, conciseness,
and comprehensiveness of the documentation. It requires consistency and
comparability across the documentation of all datasets involved, and as such
documentation must be treated as a user-centric product in and of itself. In
this paper, we propose Data Cards for fostering transparent, purposeful and
human-centered documentation of datasets within the practical contexts of
industry and research. Data Cards are structured summaries of essential facts
about various aspects of ML datasets needed by stakeholders across a dataset's
lifecycle for responsible AI development. These summaries provide explanations
of processes and rationales that shape the data and consequently the models,
such as upstream sources, data collection and annotation methods; training and
evaluation methods, intended use; or decisions affecting model performance. We
also present frameworks that ground Data Cards in real-world utility and
human-centricity. Using two case studies, we report on desirable
characteristics that support adoption across domains, organizational
structures, and audience groups. Finally, we present lessons learned from
deploying over 20 Data Cards.

本文提出了数据卡片 (Data Cards) 的概念以促进人性化的大规模模型开发过程中透明、有目的性和人性化的数据记录方法，数据卡片是必要的用户文档，提供关于机器学习数据各个方面的基本事实的结构化总结。本文描述了将数据卡片落实到实际应用的多种框架，并通过两个案例研究报告了支持领域、组织结构和观众群体的可采用特性，最后总结了 20 个数据卡片的部署经验。