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环境正义的机器学习:剖析算法方法来预测加利福尼亚州的饮用水质量
Seigi Karasaki1, Rachel Morello-Frosch2, Duncan Callaway1
1University of California Berkeley, Energy and Resources Group, Berkeley, California, United States.
The Science of the total environment
|August 26, 2024
概括
机器学习对环境科学有希望,但可以嵌入偏见. 仔细的审查至关重要,因为建模选择对饮用水质量等预测中的公平性和人口统计结果产生重大影响.
科学领域:
- 环境科学环境科学
- 环境正义 环境正义
- 数据科学是数据科学.
背景情况:
- 机器学习 (ML) 为环境监测和监管执法提供了潜力.
- 算法偏见可以延续和恶化现有的社会歧视和不平等.
研究的目的:
- 调查环境科学中使用的ML算法中嵌入偏差的潜力.
- 检查建模决策如何影响预测结果及其在不同人口群体中的公平性.
主要方法:
- 一项两部分的研究:首先,使用ML预测饮用水质量的案例研究.
- 其次,对算法选择及其对模型性能和公平性的影响进行剖析.
主要成果:
- 机器学习模型在预测饮用水质量方面表现不尽相同,有些模型准确度超过90%.
- 算法决策显著改变了预测结果,并且不成比例地影响了虚假阴性结果的人口特征.
结论:
- 对偏见的ML算法进行审查对于环境科学和司法应用至关重要.
- 研究人员和政策制定者必须采用严格的做法,以确保在ML驱动的环境管理中的公平性.
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