Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913287579369472 |
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| author | Roman, Anthony Cintron Vaughan, Jennifer Wortman See, Valerie Ballard, Steph Torres, Jehu Robinson, Caleb Ferres, Juan M. Lavista |
| author_facet | Roman, Anthony Cintron Vaughan, Jennifer Wortman See, Valerie Ballard, Steph Torres, Jehu Robinson, Caleb Ferres, Juan M. Lavista |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06153 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments Roman, Anthony Cintron Vaughan, Jennifer Wortman See, Valerie Ballard, Steph Torres, Jehu Robinson, Caleb Ferres, Juan M. Lavista Machine Learning Artificial Intelligence Human-Computer Interaction 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. |
| title | Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2312.06153 |