| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29267 |
| 来源ID | Working Paper 29267 |
| Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System | |
| Jens Ludwig; Sendhil Mullainathan | |
| 发表日期 | 2021-09-20 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Algorithms (in some form) are already widely used in the criminal justice system. We draw lessons from this experience for what is to come for the rest of society as machine learning diffuses. We find economists and other social scientists have a key role to play in shaping the impact of algorithms, in part through improving the tools used to build them. |
| 主题 | Econometrics ; Estimation Methods ; Microeconomics ; Economics of Information ; Public Economics ; Other ; Law and Economics |
| URL | https://www.nber.org/papers/w29267 |
| 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/586941 |
| 推荐引用方式 GB/T 7714 | Jens Ludwig,Sendhil Mullainathan. Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System. 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 | ||
| w29267.pdf（316KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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