Opportunities of Artificial Intelligence in Scientific Crime Detection: A Review of Global Experiences and Localization Capacities in Iran

Abstract

The increasing complexity of contemporary crime, the expansion of cybercrime and organized criminal activity, and the growing volume of digital and forensic data have exposed significant limitations in traditional methods of crime detection. Artificial intelligence has created new opportunities for scientific crime detection by enabling rapid processing of large datasets, identification of hidden criminal patterns, intelligent analysis of audiovisual and textual evidence, detection of suspicious financial transactions, reconstruction of criminal networks, and enhancement of digital forensic investigations. This study examines the principal opportunities of artificial intelligence in scientific crime detection, reviews selected international experiences, and evaluates the capacities and requirements for localizing these technologies in Iran. Using a descriptive-analytical and comparative approach, the study focuses on the technical, procedural, legal, and institutional dimensions of AI-assisted criminal investigation. The findings indicate that machine learning, deep learning, computer vision, natural language processing, biometric analysis, graph-based analytics, and intelligent anomaly detection can significantly improve the speed, accuracy, and analytical depth of crime detection. International experience also demonstrates, however, that the effectiveness of these technologies depends on data quality, transparency, explainability, human oversight, independent validation, and appropriate legal safeguards. In Iran, existing judicial, police, financial, identity, cybercrime, and administrative data infrastructures provide an important foundation for the development of AI-assisted scientific crime detection. Nevertheless, fragmented databases, limited interoperability, insufficient regulation of algorithmic evidence, risks of bias and unlawful profiling, inadequate explainability, cybersecurity concerns, and shortages of specialized human resources remain substantial obstacles. The study concludes that responsible localization requires secure inter-agency data integration, indigenous Persian-language AI models, explainable and auditable algorithms, clear rules governing the admissibility and use of AI-assisted evidence, specialized professional training, and continuous human control. Artificial intelligence should therefore function as an investigative decision-support instrument rather than an autonomous substitute for investigators, forensic experts, prosecutors, or judges.