The present study proposes a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain treatments (e.g., outreach services) to prevent violence in communities. Our rule-based engine model is the first AI-based model for victim prediction purposes. The model merit is the usage of criminology studies to construct the rule-based engine to predict victims. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, this study uses age, past criminal history, and peer influence as the main predictors of future violence. Network graph analysis is employed to measure the influence of peers on the outcome variable. The proposed model also uses logistic regression analysis to verify the variable selections in the model. Following the analytical process, the current research creates an automated model (VIPAR scores) to predict vulnerable populations for their future shooting involvements. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas the focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The proposed model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, this paper discusses the concerns about the presumption of innocence right.

本研究提出了一种新颖的自动化模型，称为高风险人群脆弱性指数（VIPAR）分数，以识别稀有人群以应对未来的枪击事件。同样，重点威慑方法可识别弱势群体并提供某些治疗措施（例如，外展服务）以防止社区暴力。我们基于规则的引擎模型是用于受害者预测的第一个基于AI的模型。该模型的优点是利用犯罪学研究来构建基于规则的引擎来预测受害者。本文旨在比较重点威慑策略列表和VIPAR得分列表，以了解它们对未来枪击受害者的预测能力。根据犯罪学研究，该研究使用了年龄，既往犯罪史，和同伴的影响是未来暴力的主要预测因素。网络图分析用于衡量同on对结果变量的影响。所提出的模型还使用逻辑回归分析来验证模型中的变量选择。经过分析过程，当前的研究创建了一个自动模型（VIPAR分数）来预测易受伤害人群的未来枪击事件。我们的经验结果表明，VIPAR分数预测了25.8％的未来枪击事件受害者和32.2％的未来枪击事件嫌疑人，而重点威慑名单则预测了13％的未来枪击事件受害者和9.4％的未来枪击行为者。在预测未来致命和非致命的枪击事件时，拟议的模型优于集中威慑政策的情报列表。此外，