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

Award IBM Best Student Paper

Ramchandani, P., H. Bastani, E. Wyatt, Working Paper, INFORMS Annual Meeting 2021 (Service Science Section), June 2021

Map of relative recruitment to sex sales pressure across the U.S., suggesting that smaller cities have higher recruitment pressure and larger cities have higher sales.

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.

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