LEVERAGING GENERATIVE AI FOR ENHANCED DETECTION OF HUMAN TRAFFICKING ONLINE

Abstract

This article introduces an innovative framework that leverages generative artificial intelligence (AI) models to detect human trafficking activities on digital platforms. Addressing the challenge of limited training data while adhering to stringent privacy and ethical standards, the approach significantly enhances detection accuracy through the strategic integration of advanced language models and synthetic data generation. The proposed architecture combines transformer-based models, convolutional neural networks, and graph neural networks to effectively analyze multifaceted trafficking patterns across diverse linguistic and cultural contexts. In addition, the framework incorporates rigorous privacy safeguards and human oversight mechanisms to ensure responsible and ethical deployment. Experimental results indicate notable improvements in detection accuracy, reduced false positive rates, and more rapid adaptation to emerging trafficking behaviors compared to conventional methodologies.