Data science can help us fight human trafficking

Relatives of victims of human trafficking wait for the sentence after an army general, two provincial politicians and police officers were among the 46 people held guilty in a court in Bangkok, Thailand, July 19, 2017. Photo by Jorge Silva/Reuters

July 30 marks the United Nations’ World Day Against Trafficking in Persons, a day focused on ending the criminal exploitation of children, women and men for forced labor or sex work.

Between 27 and 45.8 million individuals worldwide are trapped in some form of modern-day slavery. The victims are forced into slavery as sex workers, beggars and child soldiers, or as domestic workers, factory workers and laborers in manufacturing, construction, mining, commercial fishing and other industries.

Human trafficking occurs in every country in the world, including the U.S. It’s a hugely profitable industry, generating an estimated US$150 billion annually in illegal profits per year. In fact, it’s one of the largest sources of profit for global organized crime, second only to illicit drugs.

Analytics, the mathematical search for insights in data, could help law enforcement combat human trafficking. Human trafficking is essentially a supply chain in which the “supply” (human victims) moves through a network to meet “demand” (for cheap, vulnerable and illegal labor). Traffickers leave a data trail, however faint or broken, despite their efforts to operate off the grid and in the shadows.

There is an opportunity – albeit a challenging one – to use the bits of information we can get on the distribution of victims, traffickers, buyers and exploiters, and disrupt the supply chain wherever and however we can. In our latest study, we have detailed how this might work.

Finding people at risk

In most countries, resources to fight human trafficking are woefully inadequate. Agencies strive to use them as effectively and efficiently as possible, and often find themselves fighting…

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