关于科学数据论文准备在机器学习中公平透明地使用的准备情况
Joan Giner-Miguelez1,2, Abel Gómez3, Jordi Cabot4,5
1Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain. joan.giner@bsc.es.
Scientific data
|January 13, 2025
概括
本研究评估了科学数据论文的机器学习 (ML) 准备情况. 我们提出了改进数据文档的指导方针,以实现更公平,更透明的机器学习技术.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 科学出版科学出版
背景情况:
- 对于公平可靠的机器学习 (ML) 系统的日益增长的需求需要全面的数据文档.
- 科学界越来越多地采用数据共享实践,以实现可重复性,在数据论文中发布数据和文档.
- 现有的数据文档实践可能不完全符合ML应用和监管要求的需求.
研究的目的:
- 分析科学数据论文在多大程度上满足机器学习社区和监管机构的文档需求.
- 评估各种领域的科学数据论文中数据文档的覆盖范围和趋势.
- 将一般科学数据论文中的文档标准与ML特定场所的标准进行比较.
主要方法:
- 分析了来自不同科学领域的4041个数据论文的样本.
- 评估与ML应用相关的数据覆盖和文档趋势.
- 与在一个专注于ML的场所 (NeurIPS D&B) 发表的数据集进行比较分析.
主要成果:
- 在科学数据论文中识别数据文档中的差距和趋势.
- 评估当前数据纸标准对ML使用案例的适用性.
- 与ML特定数据集出版物中的文档实践进行基准测试.
结论:
- 科学数据文件显示,对ML应用的准备程度各不相同.
- 为数据创建者和出版商提出了建议,以加强数据文档.
- 这些指导方针旨在提高机器学习技术的数据透明度,公平性和可信度.
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