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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/2501.15491 |
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| _version_ | 1866909466558988288 |
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| author | Ciston, Sarah Ananny, Mike Crawford, Kate |
| author_facet | Ciston, Sarah Ananny, Mike Crawford, Kate |
| contents | Machine learning datasets are powerful but unwieldy. Despite the fact that large datasets commonly contain problematic material--whether from a technical, legal, or ethical perspective--datasets are valuable resources when handled carefully and critically. A Critical Field Guide for Working with Machine Learning Datasets suggests practical guidance for conscientious dataset stewardship. It offers questions, suggestions, strategies, and resources for working with existing machine learning datasets at every phase of their lifecycle. It combines critical AI theories and applied data science concepts, explained in accessible language. Equipped with this understanding, students, journalists, artists, researchers, and developers can be more capable of avoiding the problems unique to datasets. They can also construct more reliable, robust solutions, or even explore new ways of thinking with machine learning datasets that are more critical and conscientious. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Critical Field Guide for Working with Machine Learning Datasets Ciston, Sarah Ananny, Mike Crawford, Kate Computers and Society Machine learning datasets are powerful but unwieldy. Despite the fact that large datasets commonly contain problematic material--whether from a technical, legal, or ethical perspective--datasets are valuable resources when handled carefully and critically. A Critical Field Guide for Working with Machine Learning Datasets suggests practical guidance for conscientious dataset stewardship. It offers questions, suggestions, strategies, and resources for working with existing machine learning datasets at every phase of their lifecycle. It combines critical AI theories and applied data science concepts, explained in accessible language. Equipped with this understanding, students, journalists, artists, researchers, and developers can be more capable of avoiding the problems unique to datasets. They can also construct more reliable, robust solutions, or even explore new ways of thinking with machine learning datasets that are more critical and conscientious. |
| title | A Critical Field Guide for Working with Machine Learning Datasets |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2501.15491 |