Artificial Intelligence-Driven Predictive Policing and Its Criminal Liability Risks in Urban Crime Control

Artificial Intelligence (AI)-driven predictive policing has emerged as a transformative tool in urban crime control, offering the potential to forecast criminal activity and optimize law enforcement resource allocation. By leveraging historical crime data and advanced machine learning algorithms, these systems can identify high-risk locations and anticipate emerging crime patterns. Despite the apparent benefits in crime reduction and operational efficiency, predictive policing raises significant concerns regarding accountability, transparency, and criminal liability. The reliance on data-driven algorithms introduces potential biases, which may exacerbate existing social inequalities and disproportionately target marginalized communities. Moreover, the delegation of decision-making to AI systems challenges traditional principles of criminal justice, particularly with respect to due process, foreseeability of actions, and the attribution of liability for law enforcement outcomes. This article examines the dual role of predictive policing: its efficacy in urban crime prevention and the inherent risks it poses for criminal liability in both public and private sectors. Through a critical synthesis of recent empirical studies, legal analyses, and ethical frameworks, the study highlights the tensions between technological innovation and legal responsibility. Findings indicate that while predictive policing can reduce certain types of urban crime, the lack of clear regulatory oversight and accountability mechanisms amplifies legal and ethical risks. Policymakers and law enforcement agencies must implement rigorous governance frameworks, ensure algorithmic transparency, and maintain human oversight to mitigate liability and uphold fundamental rights. The study concludes that sustainable and equitable deployment of AI in policing requires a careful balance between operational effectiveness and adherence to legal and ethical standards.Artificial Intelligence (AI)-driven predictive policing has emerged as a transformative tool in urban crime control, offering the potential to forecast criminal activity and optimize law enforcement resource allocation. By leveraging historical crime data and advanced machine learning algorithms, these systems can identify high-risk locations and anticipate emerging crime patterns. Despite the apparent benefits in crime reduction and operational efficiency, predictive policing raises significant concerns regarding accountability, transparency, and criminal liability. The reliance on data-driven algorithms introduces potential biases, which may exacerbate existing social inequalities and disproportionately target marginalized communities. Moreover, the delegation of decision-making to AI systems challenges traditional principles of criminal justice, particularly with respect to due process, foreseeability of actions, and the attribution of liability for law enforcement outcomes. This article examines the dual role of predictive policing: its efficacy in urban crime prevention and the inherent risks it poses for criminal liability in both public and private sectors. Through a critical synthesis of recent empirical studies, legal analyses, and ethical frameworks, the study highlights the tensions between technological innovation and legal responsibility. Findings indicate that while predictive policing can reduce certain types of urban crime, the lack of clear regulatory oversight and accountability mechanisms amplifies legal and ethical risks. Policymakers and law enforcement agencies must implement rigorous governance frameworks, ensure algorithmic transparency, and maintain human oversight to mitigate liability and uphold fundamental rights. The study concludes that sustainable and equitable deployment of AI in policing requires a careful balance between operational effectiveness and adherence to legal and ethical standards.

کلیدواژه ها:

Predictive policing ، Artificial intelligence ، Urban crime control ، Algorithmic bias ، Criminal liability