| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29413 |
| 来源ID | Working Paper 29413 |
| Understanding Algorithmic Discrimination in Health Economics Through the Lens of Measurement Errors | |
| Anirban Basu; Noah Hammarlund; Sara Khor; Aasthaa Bansal | |
| 发表日期 | 2021-11-01 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | There is growing concern that the increasing use of machine learning and artificial intelligence-based systems may exacerbate health disparities through discrimination. We provide a hierarchical definition of discrimination consisting of algorithmic discrimination arising from predictive scores used for allocating resources and human discrimination arising from allocating resources by human decision-makers conditional on these predictive scores. We then offer an overarching statistical framework of algorithmic discrimination through the lens of measurement errors, which is familiar to the health economics audience. Specifically, we show that algorithmic discrimination exists when measurement errors exist in either the outcome or the predictors, and there is endogenous selection for participation in the observed data. The absence of any of these phenomena would eliminate algorithmic discrimination. We show that although equalized odds constraints can be employed as bias-mitigating strategies, such constraints may increase algorithmic discrimination when there is measurement error in the dependent variable. |
| 主题 | Econometrics ; Estimation Methods ; Health, Education, and Welfare ; Health |
| URL | https://www.nber.org/papers/w29413 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587087 |
| 推荐引用方式 GB/T 7714 | Anirban Basu,Noah Hammarlund,Sara Khor,et al. Understanding Algorithmic Discrimination in Health Economics Through the Lens of Measurement Errors. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29413.pdf（424KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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