Image credit: Equal Times

Written by Chaira Harder and Zoe Tomlinson

Edited by Alexandra Raphling

It began like an ordinary day for Linh Tran*; her friend had invited her to go shopping with a group of people in Vietnam’s capital, Hanoi. When two vehicles arrived to take the group to the city, Linh’s friend instructed her to ride in a separate car in order to “balance” the seating. What should have been a relaxing afternoon trip became a three-year nightmare. Linh found herself across the Chinese border, her friend’s vehicle nowhere to be found. Linh had been sold, becoming one of the many victims of sex trafficking.

For the next few years, Linh was locked inside a dog cage, wearing nothing but a collar. When she was brought out, it was to be teased and raped.

Today, a global estimated 40 million people are subject to human trafficking every year. Forty million, without the slightest bit of freedom. If 40 million people were to link arms, their length would extend to thrice that of the United States.

While the Covid-19 pandemic has had a detrimental effect on most businesses and industries, human trafficking has been thriving. Its recent rise has been, in part, due to climbing unemployment rates. Today, only behind the drug trade, it is the second-largest crime industry in the world, expanding faster than any other. Yet, despite its alarming numbers, talk of this modern form of slavery is deficient and shallow.

Despite its low profile, counter-trafficking work has progressed significantly due to artificial intelligence (AI). This technology provides the missing piece: strong evidence. In the past, even high suspect traffickers could easily escape consequences and further efforts due to a lack of compelling evidence.

Tech companies such as IBM, Amazon, AT&T, and Microsoft have been on the front lines of the fight against human trafficking. They’ve been actively using AI to identify patterns of suspicious activity and trace potential sources of human trafficking.

Often through the development of software and the formation of tech coalitions such as Tech Against Trafficking, these major tech companies influence other businesses, organizations, and banks to take part in counter-trafficking. The key is data sharing. Banks like Western Union and Liberty Global work with IBM, providing information to their analysts to trace the flow of funds related to trafficking through their new IBM Cloud-hosted data hub. These financial institutions train their machine learning models to recognize and detect terms and incidents specific to human trafficking. AI helps IBM identify other characteristics of human exploitation, such as the associates paid to assist in the transportation of the victims.

Other companies have taken different approaches such as conducting research to map the landscape of human trafficking issues and solutions.

Image classification, specifically facial recognition, makes hunting down traffickers more feasible. Traffic Jam is one example of successful usage of image classification. It uses patterns and algorithms to maneuver through digital advertisements and publications, detecting potential victims and criminal organizations.

Global tracking and satellite technology also utilize AI to detect suspicious activity. In 2019, a months-long investigation was finally concluded when a high-resolution satellite image captured two fishing vessels off of Papua New Guinea’s coast. 2,000 victims found on the two boats were freed from human trafficking.

Machine learning and artificial intelligence have played a crucial role in slowing the growth of the human trafficking industry. The Covid-19 pandemic brings unique threats to victims of modern day slavery, threats that can be fought and defeated with artificial intelligence. We need AI now more than ever.

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BSR. “Tech Against Trafficking.” BSR, 2020, https://www.bsr.org/en/collaboration/groups/tech-against-trafficking. Accessed 11 November 2020.

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IBM and STOP THE TRAFFIK. “Using AI to Combat Human Trafficking.” IBM, IBM, 2020, https://www.ibm.org/initiatives/human-trafficking/ht-1. Accessed 11 November 2020.

Marinus Analytics. “Traffic Jam.” Marinus Analytics, 2020, https://www.marinusanalytics.com/traffic-jam. Accessed 13 November 2020.

Niethammer, Carmen. “Cracking The $150 Billion Business Of Human Trafficking.” Forbes, Forbes, 2 February 2020, https://www.forbes.com/sites/carmenniethammer/2020/02/02/cracking-the-150-billion-business-of-human-trafficking/?sh=2212f4f74142. Accessed 11 November 2020.

Tech Against Trafficking. “Why Tech Against Trafficking?” Tech Against Trafficking, 2018, https://techagainsttrafficking.org/. Accessed 11 November 2020.

Torigoe, Yushi. “How Technology can Combat Human Trafficking.” ITU News, ITU, 30 July 2019, https://news.itu.int/how-technology-can-combat-human-trafficking/. Accessed 11 November 2020.

Yakupitiyage, Tharanga. “Fighting the World’s Largest Criminal Industry: Modern Slavery.” IPS: Inter Press Service, 19 March 2019, ipsnews.net/2019/03/fighting-the-worlds-largest-criminal-industry-modern-slavery/. Accessed 11 November 2020.

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