标题： 可解释的机器学习用于预测美国谋杀案破案情况 显示英文标题

标题： Explainable Machine Learning for Predicting Homicide Clearance in the United States

摘要： 目的：探讨可解释机器学习在预测和检测美国全国及各州已解决谋杀案的驱动因素方面的潜力。 方法：首先，比较九种算法方法，以评估在全国范围内预测已解决谋杀案的最佳性能，使用来自谋杀问责项目的数据。然后，所有算法中最准确的算法（XGBoost）用于按州预测解决结果。其次，采用SHAP（一种可解释人工智能框架）来捕捉在国家和州层面解释解决模式的最重要特征。 结果：在全国层面，XGBoost表现出总体最佳性能。在各州层面检测到显著的预测差异。在可解释性方面，SHAP突出了几个特征在一致预测调查结果中的相关性。这些包括谋杀情况、武器、受害者性别和种族，以及涉及的犯罪者和受害者的数量。 结论：可解释机器学习被证明是预测谋杀解决的有效框架。SHAP结果表明，文献中出现的两种理论观点可以更自然地整合。此外，司法管辖区的异质性强调了制定专门的州级策略以提高警方解决谋杀案绩效的重要性。 显示英文摘要

摘要： 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.