Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments
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Abstract (arXiv)
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 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 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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 39 references that are in the map. First posted on arXiv 11 Dec 2023.
Builds on
10 of 39 references.
References · 10
- 2018Datasheets for Datasetscited by 1,371
- 2021Datasets: A Community Library for Natural Language Processingcited by 526
- 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 259
- 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
- 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
- 2022The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligencecited by 26
- 2021Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cardscited by 24
- 2019Risks of Using Non-verified Open Data: A case study on using Machine Learning techniques for predicting Pregnancy Outcomes in Indiacited by 1
- 2023Open Data on GitHub: Unlocking the Potential of AIcited by 1
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2024AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Actcited by 10
- 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chaincited by 3
- 2025TechOps: Technical Documentation Templates for the AI Actcited by 2
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
- 2025TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentationcited by 0
- 2025SIEVE: Towards Verifiable Certification for Code-datasetscited by 0
- 2026AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generationcited by 0
- 2026AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation16 Mar 2026
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Tools17 Feb 2026
- 2025SIEVE: Towards Verifiable Certification for Code-datasets2 Oct 2025
- 2025TechOps: Technical Documentation Templates for the AI Act12 Aug 2025
- 2025TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation23 May 2025
- 2024AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act26 Jun 2024
- 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain20 Jun 2024