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Artificial intelligence can help in fight against human trafficking

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As part of the recent study, researchers tried to understand how can help in the fight against

A group of computational researchers, experts in (AI) and other members of the community are joining forces with policy experts, law enforcement officials, activists and survivors to help put the pieces together.

"Imagine the techniques that and are using to make a profit--understanding people, the way they connect, what their interests are, what they might buy or the activities they engage in," said Dan Lopresti,

"We can apply those same techniques--data mining, text mining, what's called graph mining--AI that's being used for legitimate and really profitable purposes, to track these illicit behaviours," added Lopresti.

The findings were discussed in the Code 8.7: Using Computational Science and AI to End Modern meeting.

Although traffickers have embraced the Internet and to recruit potential victims and advertise to customers, according to Lopresti, the same networks provide opportunities for rooting out criminal activity.

As part of the study, Lopresti helped organise a two-day conference at the in February called Code 8.7: Using Computational Science and AI to End Modern Slavery.

The conference brought together top researchers, policy makers, social scientists, representatives of the tech community and survivors for a deep dive into the topic.

The name Code 8.7 refers to Target 8.7 of the United Nations' Sustainable Development Goals which seek to end forced labour, modern slavery, and by 2030, and the worst forms of child labour by 2025.

For Lopresti, the time is ripe to move beyond our reliance on good--but fortuitous--observations to uncover crimes of It is time, he says, to to support in tackling this complex issue.

"Finding a solution to the problem of human trafficking is not just a technical one. It also involves social policy and politics. As a researcher, if you don't understand this, you could come up with a solution that you think is elegant mathematically but is totally irrelevant in the real world. So that's why we wanted to be in the same room with the social scientists and the policymakers," Lopresti explained.

Since 2015, Jennifer Gentile Long, a graduate of Lehigh and of -- a resource for prosecutors working on cases of human trafficking and gender-based violence-- and Lopresti have collaborated on computer-science-based efforts to help manage and make use of a large amount of text data in legal documents to support the organization's work in helping prosecutors build stronger cases.

"It was amazing to see experts in all these fields come together and try to coordinate efforts so that people are working toward solutions, not working haphazardly. They are making a true impact on this crime--identifying victims at points where they are missed, providing opportunities to leave and find safety, identifying perpetrators, and looking at policy in a coordinated effort. And it's so great to see Lehigh, in a way, sitting at the of the table," Long asserted.

During the conference's closing session, survivors of human trafficking shared their stories with attendees.

"It reminded everyone that even though we are talking about information, data, and policy, which all seem abstract, the data is real people. You can't treat a problem like this abstractly," said Lopresti.

"alone can't solve the problem but when we combine it with training efforts to develop highly skilled, trauma-informed investigators and prosecutors, we can enhance victim identification and safety," Long added.

Lopresti, who is an expert in document analysis and pattern recognition, is working with , prosecutors, law enforcement officials, and other computer science and engineering faculty members--Jeffrey D Heflin, Sihong Xie, and Eric PS Baumer--to help overcome the challenges of turning vast amounts of data, primarily from police incident reports, into something useable, despite limited resources.

"If an expert sits down and reads enough of these, he or she will find a common thread--this person is related to this place, which is related to this activity, which is related to this other person. The trouble is, they've got millions of these reports and just don't have enough time to read through them. We're developing natural language techniques, and techniques that are oriented to processing lots of data to identify patterns of behavior that would reflect illegal activities related to human trafficking," Lopresti said.

(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)

First Published: Wed, April 10 2019. 22:50 IST
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