Human trafficking remains an insidious, often invisible epidemic. Members of the public are increasingly aware that such activity is more widespread than meets the eye and attempt to remain alert to interpersonal signs of trafficking in their community. But there may be a better way to detect modern-day slavery using tools beyond human ability. Artificial intelligence has become a tracking tool in the fight against trafficking, sparking both praise and caution as its use requires users to balance proactivity against privacy, and ensure programmed algorithms are based on solid evidence, not stereotypes.
Source: Image by Joshua Woroniecki from Pixabay
Deep Learning to Detect Digital Trafficking
Amina Catherine Ijiga et al. (2024) investigated how advanced detection and surveillance systems use deep learning in the fight against human trafficking.[i] The researchers describe the incorporation of deep learning into advanced surveillance and detection systems as a “promising frontier” in the global fight. Exploring how deep learning algorithms have transformed surveillance methods and technologies geared to detecting distinctive patterns of trafficking activities, they describe how AI-powered surveillance has helped rescue victims and hindered the operations of human trafficking networks.
Ijiga et al. assess the effectiveness of AI systems in different contexts, ethical implications arising from the use of surveillance, the balance between security and privacy concerns, and the future potential of such technologies.
AI Goes Undercover to Fight Underground Operations
Ijiga et al. point out that traffickers often evade detection by using sophisticated approaches to enlist, transport, and ultimately exploit victims. Such clandestine operations, in combination with victim inability or reluctance to seek help, make it challenging to identify trafficking and intervene to rescue victims.
They describe deep learning as a subcategory of artificial intelligence, involving the use of “complex neural networks” to analyze and interpret large sets of data. They note the success of this technology at identifying patterns and anomalies within immense amounts of data, which they note renders it uniquely well-suited for use within systems of detection and surveillance. They note that humans can train deep learning algorithms to recognize signs and indicators of trafficking activities, such as suspicious financial activity, unusual travel patterns, and even unique language used in online advertisements. Ijiga et al. explain that by automating detection, this type of investigation can enhance both the speed and accuracy of identifying potential cases of human trafficking, which can promote more timely interventions.
Other researchers have emphasized that anti-trafficking efforts should be carefully monitored to ensure online investigation is not unduly influenced by stereotypes or biased algorithms.
Some have emphasized the need to consider the implementation of anti-trafficking technology using a cost-benefit analysis to avoid, among other things, misinterpreting information or not having the full picture before jumping to conclusions.[ii]
Artificial Assistance, Human Support
Ijiga et al. recognize the need for collaboration between law enforcement agencies and AI technology developers to maximize the positive impact of new investigative techniques. They conclude that when well-understood and used properly, deep learning applications can be critical components of the fight against human trafficking. Pairing technology with human intervention is essential to providing support to victims in real time, preparing them to enter a new life of healthy relationships, happiness, and hope.
