When we think of human trafficking, we often think about the despondent faces of women and children who live in slums all over the world. What if human trafficking is much closer to home than we think? In 2019, Markie Dell, stood on the TEDx stage to recount her experience of being a domestic human trafficking victim. She was an awkward teenager who was groomed by a girl that she befriended at a birthday party. She was subsequently kidnapped, drugged, sexually violated, intimidated at gunpoint into dancing in strip clubs for an entire year.
She didn’t know that she was a human trafficking victim until a police officer handed her a book called, “Pimpology”. Then, she knew that she was being human trafficked.
According to the Polaris Project, most human trafficking victims are trafficked by their romantic partners, spouses, family members, including parents. In the U.S., in 2018, there were 23,078 survivors identified and 10,949 cases of human trafficking. Even then, these cases are often drastically underreported.
Barbara Amaya ran away from home at the age of 12 after family members would not believe her reports of abuse from her own family. She was picked up at Dupont Circle, Washington DC by a couple that sold her to a human trafficker in New York. The trafficker reprogrammed her and trauma bonded with her. He kept her for 10 years working for him.
In her TedTalk, she said, “Does it matter, if it’s one person or a million? Are you not going to care because it’s a lot of children versus just one?”
Human Trafficking Underlies a System of Illegal Organizations and Economic Activities
Just like the COVID-19 pandemic that brought all of our global citizens together, human trafficking is often linked to global transnational organized crime. These organizations often infiltrate the real economy and impact GDP.
Just like drug trafficking, human trafficking presents lucrative profit-taking opportunities for transnational organized crime. Profits are often so lucrative that in the U.S., a trafficked teen can fetch the pimp as much as six figures a year. It’s not hard to see why vulnerable domestic populations can present themselves as enormous opportunities for the trafficker.
Children and families that use the social welfare system are vulnerable targets for traffickers. States that have a large number of families living below and on the poverty line are most vulnerable. In recent years, on social media, families have posted about their concerns with possible human traffickers following them at big box stores. Potential traffickers can follow victims around and use a variety of tactics to engage and gain access to their children.
We look at human trafficking as a part of a broad range of criminal activities of a business. It’s a part of our money laundering investigations. Identifying people and businesses involved in human trafficking often helps us identify other criminal activities as well.
In recent years, countries such as the U.S., Canada and Australia are becoming money laundering havens. Anonymous corporate ownership makes oversight into shell corporations difficult. Investigating cases of money laundering, law enforcement increasingly discover agents of domestic human trafficking are part of a larger criminal enterprise.
Unfortunately, domestic human trafficking is often seen as a more lucrative business venture than transnational human trafficking because there’s no port of entry or checkpoints to pass through when transporting from state to state. That lowers the cost significantly.
Big Data and AI are Changing the Game
Historically, when it comes to investigating domestic human trafficking cases, law enforcement has focused on locating victims and helping them escape their traffickers. In the last 10 years, in the U.S., these operations have rescued thousands of trafficking victims. They’ve had a huge impact on solving the human trafficking problem. However, they are not enough. As long as there are vulnerable populations and a supply/demand marketplace, traffickers will take advantage. With Big Data and AI, law enforcement can finally take a different approach and focus on identifying the larger crime organizations that are involved.
Big Data and AI enable us to have an impact on human trafficking today that we couldn’t 8 years or 10 years ago. As a behavioral scientist, I can draw inferences from behaviors I can see about the behaviors that I can’t see. Machine learning can help us understand patterns in seconds that a human might take several life spans to be able to understand.
Under the umbrella of Defense Advanced Research Projects Agency (DARPA), in recent years, innovative technologies were developed that are used by companies such as Marinus Analytics and Giant Oak to help law enforcement have an unprecedented view of criminal organization’s activities. This involves giving law enforcement the big picture of a criminal organization’s activities as well as the ability to pinpoint leads that will enable law enforcement to identify possible criminal relationships and activities.
Human trafficking, as a business for criminal organizations have a retail component to their business. Unlike drug trafficking, the data for human ads displayed, communications of a possible sale are often publicly available over the Internet.
If you are going to traffick humans, there needs to be a retail component of what you are doing. There needs to be a mechanism for traffickers to put up ads and make known that they have humans for sale. This is an opportunity where you have public facing data in web pages, on social media, in ads, in chats, etc.. This has to be publicly available to facilitate purchase.
In 2012, a student, Emily Kennedy from Carnegie Mellon University started to look into the issue of human trafficking and found that AI technology can be used to leverage the retail component of the Human Trafficking business to uncover human trafficking operations. She started Marinus Analytics, a company that uses artificial intelligence and machine learning in their software Traffic Jam to comb through publicly available data all over the Internet to help to identify patterns of human trafficking. Law enforcement agencies in several countries use Traffic Jam to follow up on leads generated and conduct rescue operations. This year, Marinus Analytics is a semi-finalist in the prestigious IBM Watson AI XPrize, a competition that rewards technologists who use AI to solve global issues.
Computer vision can identify the same pattern in many different photos, like the pattern in a hotel bedspread. This helps law enforcement identify multiple victims advertised and sold from the same hotel room.
Traffic Jam Visual
IMAGE FROM MARINUS ANALYTICS
We’ve had a lot of success working with law enforcement in the U.S. to help to recover victims of human trafficking. Now, we are expanding our work abroad and partnering with law enforcement overseas to help them gain insights into large organized crime groups.
Non-profit organizations such as the Anti-Human Trafficking Intelligence Initiative help to partner with law enforcement agencies and private sector companies to pool together resources in the fight against human trafficking.
We have many partners that collaborate with us to develop innovative ways in which we can us their software, tools, and resources in the fight against human trafficking. Many of the companies that we work with are practicing corporate responsibility around a number of issues, not only human trafficking. Our partnerships are mutually beneficial to the organizations we collaborate with as we help to introduce them to other organizations, financial institutions and law enforcement while receiving their assistance and resources in combating trafficking.
One of the apps that the Anti-Human Trafficking Intelligence Initiative developed is an app that can be used on the victim’s cell phones. Victims can 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. Once that occurs, victims can be rescued immediately as opposed to waiting for days.
Private and Public Sector Cooperation Enables Technology
Money laundering is at the center of any crime organization. When profits from illegitimate businesses mix with profits from legitimate businesses, it’s difficult to untangle the web of financial transactions inside the organization. Forensic Accounting is often used to track the finances of crime organizations. In the last few years, law enforcement built tools to uncover patterns of irregular financial activities. However, public sector technology still relies on data reported by the private sector banking industry.
The problem with detecting human trafficking is that it’s not easy to identify what is a good transaction from a bad one. You can have an example of a business that’s booking hotel rooms frequently. That’s showing up on bank records. With AI and machine learning, you can bring in other information to associate that with information related to human traffickers.
In the U.S., per the Bank Secrecy Act (BSA), each bank is required to send a Suspicious Activities Report (SAR) to government agencies as a part of enforcing money-laundering laws. The accuracy of this report becomes increasingly important when AI and machine learning are involved.
If your system or your bank is 95% wrong in your Suspicious Activities Report (SAR) from last year, by the way this is the standard right now, then when you bring in machine learning, you are going to very efficiently re-create all the mistakes that you made last year.
In March 2019, Dr. Shiffman testified in front of the House Subcommittee on National Security, International Development, and Monetary Policy about the importance of data cooperation between private sector banks and public sector law enforcement agencies. The legislative intent for the suspicious activities report is to help law enforcement agencies to identify terrorism, money laundering, drug trafficking, and human trafficking. But, the current implementation prevents the banks from obtaining relevant data to build good models to generate the right data to send to law enforcement to impact investigations.
The banks spend a lot of money generating SARs everyday to send it to FinCEN. In the absence of knowing which of their SARs are highly valued or not valued, the banks do not send SARs that are valuable and can impact outcomes. There needs to be a three way communication loop established between banks, regulators (FinCEN) and law enforcement. Law enforcement can inform on what data they’d like to see and the banks can cooperate in sending that information through the regulators.
Technological Cooperation Implies Responsibility
As AI and machine learning proliferate into corporate settings, there’s a real sense that the world is working closely together. A programmer in India may assist in a project located in the U.S..That project can be deployed in Europe where most of the user base are located. The underlying data that AI and machine learning learns from can come from anywhere in the world.
The world’s problems such as human trafficking, money laundering, drug trafficking and terrorisms become problems in every country and can affect the activities in many industries regardless of the size of the company and the size of the marketplace.
Responsibility still resides on the humans that are making judgments on when to use the technology and how to use the technology.
People who are in favor of technology and against technology are both a little bit wrong. By that, I mean that technology, AI and machine learning, at its core is pattern recognition. You need a good training set to identify the pattern that you want to find. You need clean data that you can run your algorithm through to identify the pattern. There could be problems in your training data. There could be problems in the data that you are running your algorithm against. You need a human in the loop to ensure that someone’s applying human reasoning, common sense, and judgement to the inputs and output.
The human factor in the usage of technology not only impacts the outcome of human trafficking investigations but ultimately has a real impact on the GDP of specific countries. From government’s loss of tax revenue, to decreased sector competition and innovation, to loss of investment, to increase in uncertainty of market conditions, there are many reasons why GDP depends on a healthy economy with minimal criminal activities.
When law enforcement and financial institutions look at human trafficking investigations as entrepreneurial business activities of criminal organizations, then it’s easy to realize the potential impact on the domestic economy as well as on the global economy.
So, who is ultimately responsible for solving this problem of human trafficking?
As machine learning and AI tells us, in the age of innovation, we have powerful technological tools to impact outcomes. But ultimately, it’s still all of us who are responsible for our collective problem of human trafficking.
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