On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning
Abstract To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolv...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04402-4 |
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| _version_ | 1832595006089592832 |
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| author | Joan Giner-Miguelez Abel Gómez Jordi Cabot |
| author_facet | Joan Giner-Miguelez Abel Gómez Jordi Cabot |
| author_sort | Joan Giner-Miguelez |
| collection | DOAJ |
| description | Abstract To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, academic institutions’ adoption of these practices has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers. In this study, we analyze how this broader scientific data documentation meets the needs of the ML community and regulatory bodies for its use in ML technologies. We examine a sample of 4041 data papers of different domains, assessing their coverage and trends in the requested dimensions and comparing them to those from an ML-focused venue (NeurIPS D&B), which publishes papers describing datasets. As a result, we propose a set of recommendation guidelines for data creators and scientific data publishers to increase their data’s preparedness for its transparent and fairer use in ML technologies. |
| format | Article |
| id | doaj-art-d938c26327a34579bf5f2cdb571a9b2e |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-d938c26327a34579bf5f2cdb571a9b2e2025-01-19T12:09:36ZengNature PortfolioScientific Data2052-44632025-01-0112111610.1038/s41597-025-04402-4On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine LearningJoan Giner-Miguelez0Abel Gómez1Jordi Cabot2Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC)Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC)Luxembourg Institute of Science and TechnologyAbstract To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, academic institutions’ adoption of these practices has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers. In this study, we analyze how this broader scientific data documentation meets the needs of the ML community and regulatory bodies for its use in ML technologies. We examine a sample of 4041 data papers of different domains, assessing their coverage and trends in the requested dimensions and comparing them to those from an ML-focused venue (NeurIPS D&B), which publishes papers describing datasets. As a result, we propose a set of recommendation guidelines for data creators and scientific data publishers to increase their data’s preparedness for its transparent and fairer use in ML technologies.https://doi.org/10.1038/s41597-025-04402-4 |
| spellingShingle | Joan Giner-Miguelez Abel Gómez Jordi Cabot On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning Scientific Data |
| title | On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning |
| title_full | On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning |
| title_fullStr | On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning |
| title_full_unstemmed | On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning |
| title_short | On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning |
| title_sort | on the readiness of scientific data papers for a fair and transparent use in machine learning |
| url | https://doi.org/10.1038/s41597-025-04402-4 |
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