Written/Edited by Ramneek Singh
New technology is constantly disrupting existing systems. In this age of disruption, it has become absolutely crucial that organizations adapt quickly to technological advancements in their field to achieve and maintain a competitive edge. For instance, certain “banks are using machine learning infused with location intelligence” to help “predict the best locations for new branches” and “solar panel companies are pairing weather data with AI-powered location-based imaging to choose the best neighborhood candidates for rooftop sun-catching technology” (Thompson). These are just two examples out of a whole pool. Tools like artificial intelligence and sophisticated computer programs have unleashed unlimited possibilities. As a result, they are instrumental in the fight against human trafficking.
Criminal organizations are not exempt from access to technology and have created smarter systems over the years to increase profitability and reduce the detection of trafficking victims. As a response to this, law enforcement and nonprofit organizations fighting against organized crime (including human trafficking) have created technological mechanisms that make detection much easier and can take down larger criminal networks. In this article, I will discuss some of these programs/systems and how they are being used in human trafficking prevention efforts, along with their overall importance in the fight against organized crime.
Many of us have probably seen or heard of some questionable advertisements posted on the internet. These ads may contain innuendos, symbols, and/or images hinting at sexual services. This has become a very popular way for pimps to get clients for their victims. An article by Forbes even claims that human trafficking has a “retail component” and “unlike drug trafficking, the data for human ads displayed” and “communications of a possible sale are often publicly available over the Internet” (Wu). To utilize this information effectively, algorithms have been created to detect patterns in such online ads. For example, Dr. Rebecca Portnoff, currently Head of Data Science at Thorn (an organization that builds technology to fight the sexual abuse of children) has created code that can take groups of such ads and determine whether they are connected (Public Affairs UC Berkeley). This is incredibly useful as traffickers want to disguise the fact that they are in charge of these individuals and that the ads are interconnected (Public Affairs UC Berkeley). Their goal is to make it seem like these are independent people. Traffickers will even go as far as using burner phones and switching email addresses to minimize the connection between the ads (Public Affairs UC Berkeley). However, Rebecca’s computer program scans through ads for similar writing styles (Public Affairs UC Berkeley). Similarities could mean the ads were placed by a single trafficker (Public Affairs UC Berkeley). This can make it easier for law enforcement to follow up on those ads and track down that trafficker. Rebecca’s code also compares the bitcoin transaction time with the time an ad was posted (Public Affairs UC Berkeley). If the two times match, there’s strong evidence that the same wallet was used to purchase that ad (Public Affairs UC Berkeley). If groups of ads have the same purchase time, all of those ads could have been purchased by the same wallet (Public Affairs UC Berkeley). This, once again, makes it much easier to hunt down the trafficker and hold them accountable for all their crimes.
Another company that is trying to detect patterns in these online ads is Marinus Analytics. Their software - Traffic Jam - uses the power of AI and machine learning to “comb through publicly available data all over the Internet to help identify patterns of human trafficking” (Wu). Traffic Jam is used by law enforcement agencies in multiple countries “to follow up on leads generated and conduct rescue operations” (Wu). According to the President and Co-founder of Marinus Analytics, Emily Kennedy, Traffic Jam employs computer vision to identify patterns in different photos such as “‘the pattern in a hotel bedspread’” (Wu). Law enforcement can use this information to “‘identify multiple victims advertised and sold from the same hotel room’” (Wu). By pinpointing a particular location, law enforcement can not only execute rescue operations but identify trafficking hotspots and hubs. This can help take down major networks.
Emily’s and Rebbeca’s work perfectly demonstrates how valuable computer programs and softwares are in aiding law enforcement. However, tools have also been designed for those being trafficked. For example, The Anti-Human Trafficking Intelligence Initiative has designed an app that allows victims to “scan QR codes that are put up in the bathrooms of hotels and other highly suspicious public places. Once the data is received, law enforcement can follow up on the lead by requesting a subpoena immediately to obtain cell phone records to verify whether it is indeed criminal activity” enabling the victim to be “rescued immediately as opposed to waiting for days” (Wu). This is a less noticeable way of getting law enforcement’s attention in comparison to making a phone call, and could significantly increase detection if made widely accessible to those at risk.
To end off, current advancements in technology have completely changed the notion of what is possible. Law enforcement is one particular sector where such advancements could positively serve the social cause. Specifically, the highly lucrative and even more disturbing human trafficking business could be abolished if these systems are used in the right manner and by the right organizations. Ultimately, the power to design these tools and initiate them lies in human hands. If we can continue to keep our morals intact while designing such intelligent resources, we can break the backbone of organized crime.
Public Affairs UC Berkeley. “Activism 2.0: Coding against Sex Trafficking.” Berkeley News,
30 Jan. 2019, news.berkeley.edu/2019/01/30/activism-2-0-coding-against-sex-trafficking/.
Thompson, Helen. “Four AI Business Applications For 2021.” Forbes, Forbes Magazine, 28
June 2021, www.forbes.com/sites/esri/2021/06/28/four-ai-business-applications-for-2021/?sh=4426d23b24da.
Wu, Jun. “AI Is Helping Us Combat The Economic Problem Of Human Trafficking.”
Forbes, Forbes Magazine, 15 Apr. 2020, www.forbes.com/sites/cognitiveworld/2020/04/14/ai-is-helping-us-combat-the-economic-problem-of-human-trafficking/?sh=664b6d1752cd.