Guardians of Tomorrow: Leveraging Responsible AI for Early Detection and Response to Criminal Threats
将物联网技术引入犯罪检测系统，基于情境犯罪预防理论提取四组特征，并加入数据合成器保护隐私，实验表明该系统能高效检测犯罪，提升边境监控效率。
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 data sets, 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. History: This paper has been accepted by Kaushik Dutta for the Special Issue on the Responsible AI and Data Science for Social Good. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0488 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0488 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .