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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.14741 |
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| _version_ | 1866913998693203968 |
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| author | Clemens-Sewall, Mary Versa Cervantes, Christopher Rafkin, Emma Otte, J. Neil Magelinski, Tom Lewis, Libby Liu, Michelle Udwin, Dana Kirkman-Bey, Monique |
| author_facet | Clemens-Sewall, Mary Versa Cervantes, Christopher Rafkin, Emma Otte, J. Neil Magelinski, Tom Lewis, Libby Liu, Michelle Udwin, Dana Kirkman-Bey, Monique |
| contents | This report provides practical guidance to teams designing or developing AI-enabled systems for how to promote trustworthiness during the data curation phase of development. In this report, the authors first define data, the data curation phase, and trustworthiness. We then describe a series of steps that the development team, especially data scientists, can take to build a trustworthy AI-enabled system. We enumerate the sequence of core steps and trace parallel paths where alternatives exist. The descriptions of these steps include strengths, weaknesses, preconditions, outcomes, and relevant open-source software tool implementations. In total, this report is a synthesis of data curation tools and approaches from relevant academic literature, and our goal is to equip readers with a diverse yet coherent set of practices for improving AI trustworthiness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CaTE Data Curation for Trustworthy AI Clemens-Sewall, Mary Versa Cervantes, Christopher Rafkin, Emma Otte, J. Neil Magelinski, Tom Lewis, Libby Liu, Michelle Udwin, Dana Kirkman-Bey, Monique Machine Learning This report provides practical guidance to teams designing or developing AI-enabled systems for how to promote trustworthiness during the data curation phase of development. In this report, the authors first define data, the data curation phase, and trustworthiness. We then describe a series of steps that the development team, especially data scientists, can take to build a trustworthy AI-enabled system. We enumerate the sequence of core steps and trace parallel paths where alternatives exist. The descriptions of these steps include strengths, weaknesses, preconditions, outcomes, and relevant open-source software tool implementations. In total, this report is a synthesis of data curation tools and approaches from relevant academic literature, and our goal is to equip readers with a diverse yet coherent set of practices for improving AI trustworthiness. |
| title | CaTE Data Curation for Trustworthy AI |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.14741 |