论文标题
可解释的机器学习，以预测美国的凶杀清除
Explainable Machine Learning for Predicting Homicide Clearance in the United States
论文作者
论文摘要
目的：探索在美国国家和州一级的杀人案驾驶员的预测和检测中，可以探索可解释的机器学习的潜力。 方法：首先，比较了九种算法方法，以评估使用谋杀责任项目中的数据，在预测清除凶杀案的国家方面的最佳绩效。然后，使用所有（XGBoost）中最准确的算法来预测在州方面的清除结果。其次，Shap是一种可解释的人工智能的框架，用于捕获最重要的特征，以解释国家和州一级的清除模式。 结果：在国家一级，XGBOOST证明可以在总体上取得最佳性能。在州方面检测到实质性的预测性变异性。在解释性方面，Shap强调了几个特征在始终预测研究结果中的相关性。这些包括凶杀情况，武器，受害者的性和种族，以及涉及的罪犯和受害者的数量。 结论：可解释的机器学习证明是预测凶杀清除率的有用框架。塑造结果表明，文献中出现了两种理论观点的更有机融合。此外，司法管辖区的异质性强调了制定临时州级策略以改善警察绩效清除凶杀案的重要性。
Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in explaining clearance patterns both at the national and state levels. Results: At the national level, XGBoost demonstrates to achieve the best performance overall. Substantial predictive variability is detected state-wise. In terms of explainability, SHAP highlights the relevance of several features in consistently predicting investigation outcomes. These include homicide circumstances, weapons, victims' sex and race, as well as number of involved offenders and victims. Conclusions: Explainable Machine Learning demonstrates to be a helpful framework for predicting homicide clearance. SHAP outcomes suggest a more organic integration of the two theoretical perspectives emerged in the literature. Furthermore, jurisdictional heterogeneity highlights the importance of developing ad hoc state-level strategies to improve police performance in clearing homicides.
