Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases
Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases
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DOI：
10.1111/jels.12276
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发表时间：
2021-03-24
影响因子：
1.7
通讯作者：
Kirchmaier, Tom
中科院分区：
文献类型：
--
作者：
Grogger, Jeffrey;Gupta, Sean;Kirchmaier, Tom
我们比较了传统的基于协议的风险评估方法和基于机器学习方法的预测。我们首先证明，传统的预测不如只利用基本失败率的简单贝叶斯分类器准确，并且具有类似的负预测错误率。基于潜在风险评估问卷的机器学习算法在假设负面预测错误比正面预测错误代价更高的情况下表现得更好。基于两年犯罪历史的机器学习模型做得更好。事实上，将基于协议的特征添加到犯罪历史中，几乎不会增加该模型的预测充分性。我们建议使用基于犯罪历史的预测来确定服务来电的优先顺序，并设计一种更敏感的工具来区分初始筛查产生的真假阳性。
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 of only 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.