| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27111 |
| 来源ID | Working Paper 27111 |
| An Economic Approach to Regulating Algorithms | |
| Ashesh Rambachan; Jon Kleinberg; Sendhil Mullainathan; Jens Ludwig | |
| 发表日期 | 2020-05-11 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | There is growing concern about "algorithmic bias" - that predictive algorithms used in decision-making might bake in or exacerbate discrimination in society. We argue that such concerns are naturally addressed using the tools of welfare economics. This approach overturns prevailing wisdom about the remedies for algorithmic bias. First, when a social planner builds the algorithm herself, her equity preference has no effect on the training procedure. So long as the data, however biased, contain signal, they will be used and the learning algorithm will be the same. Equity preferences alone provide no reason to alter how information is extracted from data - only how that information enters decision-making. Second, when private (possibly discriminatory) actors are the ones building algorithms, optimal regulation involves algorithmic disclosure but otherwise no restriction on training procedures. Under such disclosure, the use of algorithms strictly reduces the extent of discrimination relative to a world in which humans make all the decisions. |
| 主题 | Econometrics ; Estimation Methods ; Microeconomics ; Welfare and Collective Choice ; Labor Economics ; Labor Discrimination ; Other ; Law and Economics |
| URL | https://www.nber.org/papers/w27111 |
| 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/584784 |
| 推荐引用方式 GB/T 7714 | Ashesh Rambachan,Jon Kleinberg,Sendhil Mullainathan,et al. An Economic Approach to Regulating 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 | ||
| w27111.pdf（374KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
