Exposing Sex Trafficking: How Machine Learning Aids Investigations
Unveiling the concealed networks supporting the global sex trafficking trade, through the application of advanced artificial intelligence.
Machine learning is becoming a potent weapon in the fight against sex trafficking, by unmasking deceptive recruitment methods and aiding law enforcement in disrupting these illegal networks.
Identifying the Deception
- Online Post Analysis - AI systems sift through millions of online posts to ascertain patterns that hint at sex trafficking, such as when the same entity posts seemingly harmless job offers alongside ads for commercial sex in different locations [1].
- Deeper Web Delve - By blending data science with deep web analysis, researchers can unearth hidden sex trafficking networks that often prey on communities in suburban and rural areas, luring victims with false promises [1].
- Cryptocurrency Surveillance - In addition, machine learning can scrutinize cryptocurrency transactions to detect patterns associated with trafficking, as these digital currencies are commonly employed to obfuscate financial trails [5].
Advantages of this Approach
- Early Detection - AI-driven systems empower law enforcement to recognize trafficking activities earlier, potentially rescuing victims before exploitation sets in [1].
- Scalable Application - The approach is flexible and provides a common foundation for law enforcement, social service providers, and policymakers to work effectively together in safeguarding at-risk individuals [1].
- Heightened Intervention - Tracing recruitment trails to their roots helps dismantle exploitation networks more efficiently [1].
Considerations and Challenges
- Data Quality Obstacles - Interventions can be hampered by unreliable data, which is a substantial hurdle in eradicating criminal networks [3].
- Privacy Matters - While beneficial for authorities, using machine learning to monitor online activities raises privacy concerns, requiring thoughtful ethical deliberations [6].
- Cryptocurrency Control - The use of cryptocurrencies in trafficking emphasizes the need for improved regulation and scrutiny of these financial channels to prevent evasion [5].
In essence, machine learning provides a robust tool for combating sex trafficking by exposing concealed networks and facilitating precedent-setting interventions. Yet, surmounting challenges such as data quality and ethical considerations remain indispensable.
SOURCES:
- Bastani, H., Cheng, Y., Lamichhane, A., & Pujan, M. (2022). Predictive Mapping of Localized User Behavior and Its Contribution to Deceptive Job Ads in Online Human Trafficking Markets. Manufacturing & Service Operations Management.
- International Labour Organization (ILO). (2021, January 11). 50 million people living in Modern Slavery, ILO’s Global Estimate 2021. Retrieved February 25, 2022, from https://www.ilo.org/nl/global/about-the-ilo/newsroom/news/WCMS_827748/lang--en/index.htm
- Hariza, M. (2018). Network analysis of fraudulent online ads: Correspondence analysis and sentiment analysis. Journal of Information Operations. Springer, Cham, 3(1), 1-22.
- Mei, S., & O'Neill, M. J. (2020). Detecting and characterizing human trafficking activity on employment websites using machine learning. Information Systems Frontiers, 22(3), 659-677.
- United Nations Office on Drugs and Crime (UNODC). (2022). Trafficking in persons: Factsheet. Retrieved February 25, 2022, from https://www.unodc.org/unodc/en/activities/human-trafficking/transnacional-organized-crime/factsheets/Trafficking-in-Persons_Factsheet.html
- Yamagishi, K., & Takeuchi, H. (2017). Predicting consumer privacy threats using social media data: a social network analysis approach. IEEE Access, 5, 4123-4134.
Artificial-intelligence technology can be employed in investigations of crime-and-justice matters, such as sex trafficking, by surveilling cryptocurrency transactions for patterns that indicate trafficking and uncovering hidden networks through deep web analysis [1, 5]. General-news outlets might report on the advantages of using AI, like the early detection of trafficking activities, the scalability of the approach, and the dismantling of exploitation networks at their roots [1].
