Artificial intelligence (AI) has become a central element in contemporary strategies for crime prevention and public security, enabling the identification of patterns, predictive analysis, and data-driven decision-making. However, its implementation raises significant ethical, social, and institutional challenges, particularly related to algorithmic bias, opacity, and governance. In this context, this study aims to analyze the role of artificial intelligence in crime prevention from the perspective of complex thinking and algorithmic governance. The research adopts a mixed-methods sequential explanatory design. First, a semi-systematic documentary review was conducted, focusing on scientific literature published between 2018 and 2024 in databases such as Scopus, Web of Science, and Google Scholar, as well as reports from international organizations including the United Nations, UNESCO, and the OECD. Second, semi-structured interviews were carried out with eight experts from the Dominican National Police, selected through purposive sampling based on their professional experience and involvement in technological and operational processes. The findings reveal that AI systems are not neutral tools but socio-technical constructs shaped by design choices, institutional practices, and regulatory frameworks. Key challenges identified include structural bias, limited transparency, and insufficient oversight mechanisms. From a complex thinking perspective, the study highlights the need for an integrated, interdisciplinary, and ethically grounded approach to AI implementation in criminal analysis. The study concludes that effective crime prevention through AI requires not only technological advancement but also robust governance models that ensure accountability, fairness, and meaningful human oversight.
Jose Dicent (Thu,) studied this question.
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