Human trafficking continues to be a widespread global problem, requiring sophisticated technological approaches to assist in its identification and suppression. This systematic literature review investigates the present status of algorithmic methods for detecting human trafficking, with particular attention to their methodologies, challenges, and possible future developments. We synthesize existing research across four key dimensions: detection methods, analytical frameworks, strategic interventions, and ancillary contributions. The review outlines a varied range of methods, such as machine learning, natural language processing, and network analysis, that have been applied to identify trafficking activities in online and offline settings. Although these approaches appear promising, major obstacles remain, including limited data availability, ethical issues, and the constantly changing structure of trafficking networks. Our study shows interdisciplinary cooperation and the merging of specialized knowledge are essential for advancing algorithmic outcomes. Furthermore, we highlight gaps in current research, particularly in real-world deployment and scalability, and propose actionable recommendations for future work. The results highlight the necessity for strong, clear, and morally grounded algorithms capable of effectively aiding anti-trafficking initiatives. This review synthesizes current understanding and highlights new developments to direct researchers and professionals toward more effective strategies in addressing human trafficking.
Laszlo Pokorny Dr. Laszlo Pokorny (Fri,) studied this question.