شعیب صبار

Background: Since the advent of modern policing, technological innovations in communication and information management have significantly shaped investigative practices and crime detection strategies. Aims: The current research study explores the transformative role of Artificial Intelligence in modern crime detection and prevention across diverse domains including cybercrime, environmental crime, financial fraud, and urban surveillance. Methodology: Employing a qualitative meta-synthesis methodology, the research critically examines peer-reviewed literature published between the years 2015 and 2025 to identify emerging trends, technological innovations, and socio-legal implications associated with AI-driven policing. Findings: The key findings highlight the integration of machine learning, computer vision, and natural language processing techniques into the predictive and real-time law enforcement systems. These technologies have demonstrably enhanced the accuracy, efficiency, and responsiveness of crime prevention strategies. However, the current study also reveals significant challenges, including algorithmic bias, lack of transparency, and inadequate regulatory oversight, particularly in socially stratified or underregulated contexts. Conclusion: The current article underscores the necessity of embedding explainability, accountability, and human oversight into Artificial Intelligence systems to ensure the ethical and equitable implementation of AI-driven policing systems.

This study investigates the way in which public discourse on social media reflects and shapes global power dynamics surrounding AI. Leveraging a corpus of approximately 21,000 English-language posts from Platform X (2021–2025), this study utilizes a computational linguistics framework—incorporating topic modeling, sentiment analysis, emotion classification, and named entity recognition—to analyze the construction of AI, interrogating its thematic narratives and affective investments across geopolitical contexts. Findings reveal a discourse shaped by U.S.–China technological rivalry, AI militarization, and infrastructural sovereignty, with strong currents of fear, anger, and skepticism. While Western powers and corporate actors dominate the narrative space, alternative discourses from the Global South emphasize digital dependency, exclusion, and justice. The emotional intensity and thematic complexity of the discourse suggest that publics are not simply reacting to geopolitical developments, but actively construct contested imaginaries of AI’s role in world order. This research contributes to a growing body of literature that recognizes public discourse as a critical site of informal geopolitics and underscores the need for more inclusive, responsive, and ethically grounded AI governance frameworks.

This study presents a structured literature review on the application of AI in credit risk assessment, synthesizing empirical and conceptual research published between 2016 and 2022. It critically examines a range of AI models, including artificial neural networks (ANN), support vector machines (SVM), fuzzy logic systems, and hybrid architectures, with an emphasis on their predictive accuracy, robustness, and operational applicability. The review highlights that AI-based models consistently outperform traditional statistical techniques in handling nonlinear patterns, imbalanced datasets, and complex borrower profiles. Furthermore, AI enhances the inclusivity of credit evaluation by integrating alternative data sources and adapting to dynamic financial environments. However, the study also identifies ongoing challenges related to model interpretability, fairness, and regulatory compliance. By evaluating model performance metrics and methodological innovations across multiple contexts—including emerging markets, peer-to-peer platforms, and digital banking—the study offers a nuanced understanding of AI's strengths and limitations. The paper concludes with a call for balanced integration of explainable AI tools and ethical governance to ensure responsible deployment in financial institutions.

This study explores how AI is perceived and operationalized in credit risk assessment within Iranian banking institutions, with a particular focus on the experiences of electronic banking professionals in Tehran. Drawing on grounded theory methodology and semi-structured interviews with 38 practitioners from both public and private banks, the research reveals a complex landscape of technological promise and institutional constraint. Participants emphasized the efficiency, consistency, and expanded analytical reach afforded by AI models, particularly in leveraging alternative data and enhancing fraud detection. However, these benefits are tempered by operational challenges, including fragmented data systems, outdated IT infrastructure, and opaque algorithmic outputs. Ethical and regulatory concerns—especially surrounding algorithmic bias, accountability, and the absence of formal oversight—emerged as significant barriers to responsible deployment. Moreover, organizational resistance, hierarchical decision-making structures, and cultural skepticism toward automation further complicate adoption. The findings suggest strong practitioner support for hybrid decision-making models that integrate AI capabilities with human expertise. This model offers a viable pathway toward responsible innovation, balancing the computational advantages of AI with the contextual judgment and ethical sensitivity of human agents.