EVOLVING PARADIGMS IN CRIME PREDICTION: NEURAL ARCHITECTURES AND ETHICAL CONSIDERATIONS IN URBAN SAFETY

Abstract

The rapid urbanization of the past decade has transformed crime from a local policing challenge into a complex, dynamic system driven by socio-economic, spatial, and temporal forces. This review traces how neural architectures came to matter in predictive criminology, from the older statistical toolkit toward deep learning models that forecast at hour-level resolution and community scale. Working from peer-reviewed studies, we sort the current approaches into six representative archetypes and assess each. The verdict is mixed. Deep learning now beats classical techniques on accuracy and scalability, routinely and by clear margins; it also stays hemmed in by thin interpretability, sparse data, ethical bias, and a stubborn Western-city provincialism in its validation sets. Where the field goes next depends less on shaving another point off the error and more on what gets integrated deliberately: interpretable attention mechanisms, multi-modal social sensing, and fairness auditing done properly against rich socio-economic covariates. Trust is the binding constraint on deployment. Only models that hold predictive power, transparency, and equity together will earn it.