数据集营养标签（第二代）：利用上下文减轻人工智能的危害
The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence
摘要：
随着数据集的产生和对数据集的依赖以产生自动决策系统（ADS）的增加，对评估和询问基础数据的过程的需求也在增加。在2018年推出数据集营养标签后，数据营养项目对标签的设计和目的进行了重大更新，并将于2020年底推出更新后的标签，本文对此进行了预览。新标签包括通过更新的设计和针对数据科学家配置文件的用户界面呈现的特定于上下文的用例和警报。本文讨论了标签旨在减轻的基础训练数据的危害和偏差，工作的当前状态，包括正在标记的新数据集，新的和现有的挑战，以及工作的进一步方向，以及预览新标签的图。
英文摘要：
As the production of and reliance on datasets to produce automated decision-making systems (ADS) increases, so does the need for processes for evaluating and interrogating the underlying data. After launching the Dataset Nutrition Label in 2018, the Data Nutrition Project has made significant updates to the design and purpose of the Label, and is launching an updated Label in late 2020, which is previewed in this paper. The new Label includes context-specific Use Cases &Alerts presented through an updated design and user interface targeted towards the data scientist profile. This paper discusses the harm and bias from underlying training data that the Label is intended to mitigate, the current state of the work including new datasets being labeled, new and existing challenges, and further directions of the work, as well as Figures previewing the new label.
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。
