Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning

31 Pages Posted: 18 Jun 2021 Last revised: 4 Aug 2022

See all articles by Pia Ramchandani

Pia Ramchandani

University of Pennsylvania - The Wharton School

Hamsa Bastani

University of Pennsylvania - The Wharton School

Emily Wyatt

affiliation not provided to SSRN

Date Written: June 13, 2021

Abstract

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. We leverage massive deep web data (collected globally from leading commercial sex websites) in tandem with a novel machine learning framework to unmask suspicious recruitment-to-sales pathways, thereby providing the first global network view of trafficking risk in commercial sex supply chains. This allows us to infer likely recruitment-to-sales trafficking routes of criminal entities, deceptive approaches used to recruit victims, and regional variations in recruitment vs. sales pressure. These insights can help law enforcement agencies along trafficking routes better coordinate efforts, as well as target local counter-trafficking policies and interventions towards exploitative behavior frequently exhibited in that region.

Keywords: sex trafficking, commercial sex supply chains, recruitment, deep web, machine learning

Suggested Citation

Ramchandani, Pia and Bastani, Hamsa and Wyatt, Emily, Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning (June 13, 2021). Available at SSRN: https://ssrn.com/abstract=3866259 or http://dx.doi.org/10.2139/ssrn.3866259

Pia Ramchandani

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Hamsa Bastani (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Emily Wyatt

affiliation not provided to SSRN

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