Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning
利用深度网络数据和机器学习框架，识别商业性广告中隐藏的欺骗性招聘模式，揭示人口贩卖高风险路径，帮助执法机构和社会政策制定者打击贩卖活动。
Problem definition: The covert nature of sex trafficking provides a significant barrier to generating large-scale, data-driven insights to inform law enforcement, policy, and social work. Existing research has focused on analyzing commercial sex sales on the internet to capture scalable geographical proxies for trafficking. However, ads selling commercial sex do not reveal information about worker consent. Therefore, it is challenging to identify risk for trafficking, which involves fraud, coercion, or abuse. Methodology/results: We leverage massive deep web data (collected globally from leading commercial sex websites) in tandem with a novel machine learning framework (combining natural language processing, active learning, and network analysis) to study how and where sex worker recruitment occurs. This allows us to unmask potentially deceptive recruitment patterns (e.g., an entity that recruits for modeling but sells sex), which signal high trafficking risk. We demonstrate via simulations that our approach outperforms existing active learning techniques to identify key nodes and edges in the underlying trafficking network. Our analysis provides a geographical network view of online commercial sex supply chains, highlighting deceptive recruitment-to-sales pathways that are likely trafficking routes. Managerial implications: Our results can help law enforcement agencies along trafficking routes better coordinate efforts to tackle trafficking entities at both ends of the supply chain, as well as target local social policies and interventions toward exploitative recruitment behavior frequently exhibited in that region. History: This paper was selected for Manufacturing and Services Operations Management journal from the 2022 MSOM Sustainable Operations SIG Conference. Funding: The Wharton AI & Analytics Initiative funded this study. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0304 .