用机器学习发现保险评估偏差加剧了巴尔的摩空气质量不公
Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
- 结合保险评估、人口与污染数据，分析城市空气质量差异
- 低收入街区与高收入街区NO2水平差距显著，种族间污染暴露不均
- 揭示历史政策遗留问题对当代环境公平的影响，适合政策研究者
本研究将机器学习与软件工程方法应用于巴尔的摩市空气质量分析。数据模型整合三大来源：1）房主贷款公司使用的有偏保险风险估算方法；2）巴尔的摩居民人口统计信息；3）美国202个主要城市的普查数据对NO2和PM2.5浓度的估计。数据涵盖650,643名巴尔的摩居民及4470万全美居民。结果表明，空气污染水平与有偏的保险评估方法存在明显关联。高收入街区与低收入街区之间存在显著的NO2浓度差异，不同族裔群体间的空气污染暴露也存在类似不平等。由于巴尔的摩市有色人种比例较高，研究揭示了长期政策歧视如何持续影响市民生活质量。
原文摘要 · Abstract (English)
In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution levels have a clear association with the biased insurance estimating method. Great disparities present in NO2 level between more desirable and low income blocks. Similar disparities exist in air pollution level between residents' ethnicity. As Baltimore population consists of a greater proportion of people of color, the finding reveals how decades old policies has continued to discriminate and affect quality of life of Baltimore citizens today.
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