| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28222 |
| 来源ID | Working Paper 28222 |
| Measuring Racial Discrimination in Algorithms | |
| David Arnold; Will S. Dobbie; Peter Hull | |
| 发表日期 | 2020-12-21 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | There is growing concern that the rise of algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such algorithmic discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this challenge and measure algorithmic discrimination in the setting of pretrial bail decisions. We first show that the selection challenge reduces to the challenge of measuring four moments: the mean latent qualification of white and Black individuals and the race-specific covariance between qualification and the algorithm’s treatment recommendation. We then show how these four moments can be estimated by extrapolating quasi-experimental variation across as-good-as-randomly assigned decision-makers. Estimates from New York City show that a sophisticated machine learning algorithm discriminates against Black defendants, even though defendant race and ethnicity are not included in the training data. The algorithm recommends releasing white defendants before trial at an 8 percentage point (11 percent) higher rate than Black defendants with identical potential for pretrial misconduct, with this unwarranted disparity explaining 77 percent of the observed racial disparity in algorithmic recommendations. We find a similar level of algorithmic discrimination with regression-based recommendations, using a model inspired by a widely used pretrial risk assessment tool. |
| 主题 | Econometrics ; Estimation Methods ; Labor Economics ; Demography and Aging ; Other ; Law and Economics |
| URL | https://www.nber.org/papers/w28222 |
| 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/585896 |
| 推荐引用方式 GB/T 7714 | David Arnold,Will S. Dobbie,Peter Hull. Measuring Racial Discrimination in Algorithms. 2020. |
| 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 | ||
| w28222.pdf（644KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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