As part of the recent study, researchers tried to understand how artificial intelligence can help in the fight against human trafficking.
A group of computational researchers, experts in artificial intelligence (AI) and other members of the technology community are joining forces with policy experts, law enforcement officials, activists and survivors to help put the pieces together.
“Imagine the techniques that Google and Facebook 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, lead researcher.
"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 social media platforms 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 United Nations 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 human trafficking 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 human trafficking. It is time, he says, to leverage technology to support trained law enforcement 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 chief executive officer of Aequitas -- 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 AEquitas 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 head 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.
"Technology 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 RIIC Director Julia Kocis , prosecutors, law enforcement officials, and other Rossin College 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, text mining and data mining 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.