In this paper, we proposed 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 types of treatments (e.g., outreach services) to prevent violence in communities. The proposed rule-based engine model is the first AI-based model for victim prediction. 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, the model uses age, past criminal history, and peer influence as the main predictors of future violence. Social network analysis is employed to measure the influence of peers on the outcome variable. The model also uses logistic regression analysis to verify the variable selections. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, we discuss the concerns about the presumption of innocence right.

中文翻译：

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基于规则的受害者预测模型

在本文中，我们提出了一种新的自动化模型，称为风险人口脆弱性指数 (VIPAR) 评分，以识别稀有人群以应对未来的枪击受害情况。同样，集中威慑方法识别弱势个体并提供某些类型的治疗（例如外展服务）以防止社区暴力。提出的基于规则的引擎模型是第一个基于 AI 的受害者预测模型。本文旨在将重点威慑策略列表与 VIPAR 评分列表进行比较，以了解它们对未来射击受害的预测能力。借鉴犯罪学研究，该模型使用年龄、过去的犯罪历史和同伴影响作为未来暴力的主要预测因素。社交网络分析用于衡量同伴对结果变量的影响。该模型还使用逻辑回归分析来验证变量选择。我们的实证结果表明，VIPAR 分数预测了 25.8% 的未来枪击受害者和 32.2% 的未来枪击嫌疑人，而重点威慑列表预测了 13% 的未来枪击受害者和 9.4% 的未来枪击嫌疑人。该模型在预测未来致命和非致命枪击事件方面优于集中威慑政策的情报清单。此外，我们讨论了对无罪推定权的关注。该模型在预测未来致命和非致命枪击事件方面优于集中威慑政策的情报清单。此外，我们讨论了对无罪推定权的关注。该模型在预测未来致命和非致命枪击事件方面优于集中威慑政策的情报清单。此外，我们讨论了对无罪推定权的关注。