Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments
- Anthony Cintron Roman ,
- Jennifer Wortman Vaughan ,
- Valerie See ,
- Steph Ballard ,
- Nicolas Schifano ,
- Jehú Torres ,
- Caleb Robinson ,
- Juan M. Lavista Ferres
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.
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Open datasheets framework
April 15, 2024
This framework aims to assist in the documentation of datasets to promote transparency and help dataset creators and consumers make informed decisions. You can read more about it in our paper: Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments