This paper introduces a no-code, machine-readable documentation framework for
open datasets, with a focus on Responsible AI (RAI) considerations. The
framework aims to improve the accessibility, comprehensibility, and usability
of open datasets, facilitating easier discovery and use, better understanding
of content and context, and evaluation of dataset quality and accuracy. The
proposed framework is designed to streamline the evaluation of datasets,
helping researchers, data scientists, and other open data users quickly
identify datasets that meet their needs and/or organizational policies or
regulations. The paper also discusses the implementation of the framework and
provides recommendations to maximize its potential. The framework is expected
to enhance the quality and reliability of data used in research and
decision-making, fostering the development of more responsible and trustworthy
AI systems.

介绍了一种无代码、机器可读的开放数据集文档框架，关注负责任的人工智能考虑。旨在提高开放数据集的可访问性、可理解性和可用性，促进更容易发现和使用、更好理解内容和背景以及评估数据集的质量和准确性。该框架旨在简化数据集评估，帮助研究人员、数据科学家和其他开放数据用户快速识别满足其需求和 / 或组织政策或法规的数据集。论文还讨论了该框架的实施，并提供了最大化其潜力的建议。预期该框架将提高研究和决策中使用的数据的质量和可靠性，促进更负责任和值得信赖的人工智能系统的发展。