  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.
