TY  - JOUR
PY  - 2022//
TI  - Explainable machine learning for predicting homicide clearance in the United States
JO  - Journal of criminal justice
A1  - Campedelli, Gian Maria
SP  - e101898
EP  - e101898
VL  - 79
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0047-2352
UR  - http://dx.doi.org/10.1016/j.jcrimjus.2022.101898
ID  - ref1
ER  -