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Comparing Conventional and Machine‐Learning Approaches to Risk Assessment in Domestic Abuse Cases
Journal of Empirical Legal Studies ( IF 1.268 ) Pub Date : 2021-03-24 , DOI: 10.1111/jels.12276
Jeffrey Grogger, Sean Gupta, Ria Ivandic, Tom Kirchmaier

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.

中文翻译:

比较传统方法和机器学习方法进行家庭虐待案例的风险评估

我们将传统的基于协议的方法进行风险评估的预测与基于机器学习的方法进行的预测进行比较。我们首先表明,传统的预测方法不如简单的仅使用基本故障率的贝叶斯分类器准确,并且其负预测误差率也与之相似。在负预测误差比正预测误差代价更高的假设下,基于潜在风险评估调查表的机器学习算法的效果更好。基于两年犯罪历史的机器学习模型甚至更好。的确,将基于协议的功能添加到犯罪记录中几乎不会增加模型的预测充分性。我们建议使用基于犯罪记录的预测来优先考虑服务请求,
更新日期:2021-04-05
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