| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28293 |
| 来源ID | Working Paper 28293 |
| Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases | |
| Jeffrey Grogger; Sean Gupta; Ria Ivandic; Tom Kirchmaier | |
| 发表日期 | 2021-01-04 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that makes use only of the base failure rate. Machine learning algorithms based on the underlying risk assessment questionnaire do better under the assumption that negative prediction errors are more costly than positive prediction errors. Machine learning models based on two-year criminal histories do even better. Indeed, adding the protocol-based features to the criminal histories adds little to the predictive adequacy of the model. We suggest using the predictions based on criminal histories to prioritize incoming calls for service, and devising a more sensitive instrument to distinguish true from false positives that result from this initial screening. |
| 主题 | Other ; Law and Economics |
| URL | https://www.nber.org/papers/w28293 |
| 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/585966 |
| 推荐引用方式 GB/T 7714 | Jeffrey Grogger,Sean Gupta,Ria Ivandic,et al. Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases. 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 | ||
| w28293.pdf（1113KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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