Guardians of Tomorrow: Leveraging Responsible AI for Early Detection and Response to Criminal Threats
Year: 2025
Type: article
Abstract: Crime detection is crucial for creating and sustaining peaceful societies. In this study, we introduce Internet of Things (IoT) technology into the development of crime detection systems. Utilizing the situational crime prevention (SCP) theory from criminology, we propose a feature engineering method to extract IoT-based features that describe, explain, and predict criminal activities. In addition to commonly used features in traditional crime detection, we derive four groups of features based on SCP: criminal efforts, criminal risks, anticipated rewards, and excuses. In addition, to address growing concerns about IoT privacy issues, we incorporate a data synthesizer into our framework to generate privacy-preserving data similar to the original, allowing predictive models to be trained without accessing private information. The synthesizer uses a Bayesian network model combined with differential privacy techniques through the Laplace mechanism. Our results, based on real-world datasets, demonstrate that the proposed IoT-enabled crime detection system can achieve high-performance crime detection and has the potential to increase border surveillance efficiency with limited police resources. Our study highlights the power of artificial intelligence (AI) analytics and provides a viable framework solution for the responsible development of AI-based systems.
Source: INFORMS journal on computing
Authors: Xiaotong Sun, Qili Wang, Liangfei Qiu, Wei Xu
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4410855723