Data Cards：为负责的AI提供有目的和透明的数据集文档
Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI
摘要：
随着研究和工业转向能够执行众多下游任务的大规模模型，理解赋予模型细微差别的多模态数据集的复杂性迅速增加。对数据集的起源、发展、意图、道德考虑和演变的清晰和透彻的理解，是负责任和知情地部署模型的必要步骤，特别是在面向人的环境和高风险领域。然而，这种理解的负担往往落在文档的可理解性、简洁性和全面性上。它要求所有相关数据集的文档具有一致性和可比性，因此文档本身必须被视为以用户为中心的产品。在本文中，我们提出了数据卡，用于在工业和研究的实际背景下促进透明，有目的和以人为本的数据集文档。数据卡是关于ML数据集各个方面的基本事实的结构化摘要，这些数据集是利益相关者在数据集生命周期中进行负责任的AI开发所需的。这些总结提供了对形成数据和模型的过程和原理的解释，例如上游来源、数据汇集和注释方法;培训和评价方法、预期用途;或影响模型性能的决策。我们还提出了在现实世界中的公用事业和以人为本的数据卡接地框架。使用两个案例研究，我们报告的理想特性，支持跨域，组织结构和受众群体的采用。最后，我们介绍了部署20多个数据卡的经验教训。
英文摘要：
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.
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。
