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Illicit massage businesses (IMBs) run by human trafficking rings are rampant in the United States. A �鶹��Ƶ professor has helped build what may be the best artificial intelligence (AI)-driven tool to root them out. 

Human trafficking rings are at their most dangerous when they masquerade as legitimate commercial activity. IMBs are one of the most common ways in which exploitive networks operate in plain sight. 

, an anti-trafficking nonprofit, estimates that there are more than 13,000 IMBs active in the United States, raking in annual total revenue of more than $5 billion. 

“You are stuck in a massage business. You’re not allowed to go out,” says , assistant professor of information systems and operations management at the Donald G. Costello College of Business at George �鶹��Ƶ, describing the plight of IMB workers. “Your passports are taken away, and you’re supposed to do a certain amount of business every day and give the money to the trafficker. It’s a really abhorrent form of abuse.” 

Ray is one of a growing group of researchers exploring how various forms of AI could help resource-constrained law enforcement agencies differentiate between IMBs and the legitimate enterprises they try to mimic. His ongoing research using graph neural networks has yielded more promising results than rival approaches, when put to the test in a recent experiment. 

His co-researchers on the IMB project are Lumina Albert and Swetha Varadarajan of Colorado State University. 

According to Ray, “Graph neural networks are just a fancy way of saying that if I get a graph of a city or locality at one point in time, and I add data to it, can I predict future patterns on this graph if I know the past?” 

This approach made sense for detecting IMBs, because try as they might to appear above board, they have geographical needs that conventional businesses don’t. “IMBs don’t allow their trafficked employees to go out of the parlor,” Ray says. “But since they’re humans, they need sustenance. They have to be near groceries, gas stations, where they can get stuff and come back.” 

The researchers combined several graph neural networks into a framework called IMBWatch. The training data-set for IMBWatch comprised publicly available information such as online customer reviews, arrest and raid data for known IMBs, and advertisements from websites promoting illicit activities (e.g., the infamous Backpage). The result, in essence, was a series of snapshots mapping the evolution of the IMB network in a given city or county over a period of time. This could then be overlaid on geographical maps to tease out hidden patterns. 

To gauge ���ѵ��²��ٳ���’s relative performance, the researchers let it loose on a testing data-set alongside four other AI models, which were not as sensitive to the nuanced interplay of spatial and temporal factors. Of the five models, IMBWatch provided the most accurate, precise and informative predictions. In other words, it outperformed the others at spotting IMBs among a larger mass of local businesses. 

While encouraging, these outcomes require further confirmation with a larger data-set. “IMBWatch was trained on data from Georgia and Louisiana, not the entire United States,” Ray says. “These were small, manageable data-sets, but we will now scale up to major states such as New York and California.” 

The researchers are also looking at enhancing IMBWatch with data related to how workers end up wandering into trafficking webs. These might include “proximity to hospitals, religious places, etc. because a lot of times people are coerced by religious compulsions, or because they’re pregnant and need some care,” Ray says. 

This is not Ray’s first foray into the field of AI-fueled anti-trafficking. Previously, Ray co-developed a model for of human trafficking activity at transit stations and on fishing vessels. 

However, law enforcement agencies and other pertinent stakeholders (e.g., business owners) are often wary of adopting AI-based solutions, due to a lack of trust in the technology. Ray and his co-researchers are currently devising a framework that will clarify how these stakeholders can work together with tech experts and, perhaps most importantly, human trafficking survivors to make the best possible use of AI.  

“This qualitative piece is required to make sure that people who are on the sidelines, on the fences about using this actually start using it, because that’s the need right now,” Ray says.