FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing

FASE is a Fairness-Aware Spatiotemporal Event Graph framework designed to integrate crime prediction with fairness-constrained patrol allocation to mitigate racial disparities in predictive policing. It utilizes a spatiotemporal graph neural network and a multivariate Hawkes process to model crime incidents in Baltimore, addressing data bias through a closed-loop deployment simulator.