Pablo Rivas, Ph.D., and Tomas Cerny, Ph.D., from Baylor’s Computer Science Department are part of a more than $300,000 grant addressing human trafficking, stolen auto part sales and more
By Derek Smith, Baylor University Marketing & Communications
WACO, Texas (Oct. 13, 2022) – Many people use consumer-to-consumer web sites for common household transactions. These sites, like Craigslist, connect buyers and sellers for a variety of legal transactions. Unfortunately, criminals likewise utilize these consumer-to-consumer websites but do so to facilitate illicit business in human trafficking, the sale of stolen goods and more. It’s these types of transactions that two Baylor University professors and an interdisciplinary team of computer scientists are looking to thwart.
, assistant professor of computer science in Baylor’s , is the principal investigator on a $314,284 grant from the ³Ô¹ÏÍøÕ¾ Science Foundation (NSF) to utilize technology to identify and disrupt illicit transactions online. Baylor colleague , assistant professor of computer science, also serves on the five-person team.
“We are looking at this from two perspectives,” Rivas said. “One is for human services being offered illegally, with the goal of detecting human trafficking. Second, what we learn from this domain can be applied to other transactions, like stolen goods such as automobile parts.”
The project sits at the intersection of emerging technologies and human challenges. The NSF funding will fuel the research team as they apply their discipline in a way that could serve individuals in need of an advocate.
“Innovation doesn’t happen by accident, and Baylor is fortunate to have people as capable and passionate as Dr. Rivas and Dr. Cerny on our faculty,” said Erich Baker, Ph.D., interim dean of the School of Engineering and Computer Science. “Given the technology the world uses today, this work is vital, and we’re pleased NSF sees the value of investing in this team.”
Risk and reward using natural language processing
The award funding the work is an EAGER SaTC grant from the NSF, promoting a secure and trustworthy cyberspace. Entitled , the grant funds the team’s research using Natural language processing (NLP). NLP is considered a subfield of artificial intelligence (AI) involving human language patterns which pursues an understanding of language, context, information and more shared online.
Rivas, Cerny and their team will seek to determine NLP’s ability to identify suspicious listings online.
“NLP has been around for a long time, but computational linguistics and increased compute power in combination with machine learning breakthroughs has pushed the field to new exciting frontiers,” Rivas said. “With machine learning, we can thrust NLP to make inferences by detecting patterns in language.”
As an EAGER SaTC grant, the NSF recognizes the potential for risk and reward. These projects are, by nature, experimental. However, if they work, they advance safety and security for internet users. The risk, Rivas said, is that researchers don’t know what they’ll find. But, the goal of disrupting illicit activity, identifying individuals caught in trafficking and making it harder to engage in such activity is a goal worthy of that investment.
“We believe we will find markers or identifiers of human trafficking, but that’s the risk,” Rivas said. “We similarly hope we can apply what we learn to other illegal activity online.”
An interdisciplinary team promoting safety online
In addition to Rivas and Cerny, a Baylor alumnus and former Baylor professor serve on the grant team. Laurie Giddens, Ph.D., assistant professor of information systems at the University of North Texas, MSIS ’02, Ph.D. ’17, is a two-time Baylor graduate who partnered with her then-Baylor professor, Stacie Petter, Ph.D., on a forming an interdisciplinary team to examine obstacles and training to equip law enforcement to fight human trafficking. Petter, now a professor of management information systems at Wake Forest University, continues to serve on this grant, along with Gisella Bichler, Ph.D., from the Center for Criminal Justice Research at California State University San Bernardino, and Javier Turek, Ph.D., research scientist in machine learning at Intel Labs.
Rivas, an AI expert, provides insights into machine learning and ethics, an intersection in which he is well-versed. He serves as the director for , funded through a prior NSF grant. Likewise, Cerny provides insights through a software lens.
“I am looking at the data science perspective through the software engineering side,” Cerny said. “This is about how to fetch and store data and to make semantic connections based on commonly recognized principles in our discipline.”
The grant, which runs through April 2024, enables Rivas and collaborators to utilize their discipline to make crime harder to commit and those victimized by crime easier to identify.
“We believe that, with machine learning, we can create models that can help us learn more about how crime works,” Rivas said. “We can then provide that intelligence to others, like law enforcement or behavioral scientists, to recognize those engaged in illegal activity and connect law enforcement to them, so they account for their doings. That leads to a safer online community for everyone using these consumer-to-consumer sites and disrupts illegal activity.”
