Using artificial intelligence to detect human trafficking

Human trafficking remains an insidious, often invisible epidemic. The public is increasingly aware that such activities are more widespread than meets the eye and is trying to remain alert to interpersonal signs of human trafficking in their community. But there may be a better way to uncover modern slavery using tools that go beyond human capabilities. Artificial intelligence has become a tracking tool in the fight against human trafficking, drawing both praise and caution, as its use requires users to balance proactivity with privacy and ensure that programmed algorithms are based on solid evidence, not on Based on stereotypes.

Source: Image by Joshua Woroniecki from Pixabay

Deep learning for digital human trafficking detection

Amina Catherine Ijiga et al. (2024) examined how advanced detection and surveillance systems leverage deep learning in the fight against human trafficking.(i) The researchers describe the integration of deep learning into advanced surveillance and detection systems as a “promising frontier” in the global fight. They examine how deep learning algorithms have transformed surveillance methods and technologies aimed at identifying distinctive patterns of human trafficking activity, and describe how AI-powered surveillance has helped rescue victims and hindered the work of human trafficking networks.

Ijiga et al. Evaluate 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 combat underground operations

Ijiga et al. point out that human traffickers often evade detection by using sophisticated methods to recruit, transport and ultimately exploit victims. Such sting operations, combined with the victim’s inability or reluctance to seek help, make it difficult to detect human trafficking and intervene to rescue victims.

They describe deep learning as a subcategory of artificial intelligence that uses “complex neural networks” to analyze and interpret large amounts of data. They point to the technology’s success in identifying patterns and anomalies in massive amounts of data, which they say makes it particularly well-suited for use in detection and surveillance systems. They note that people can train deep learning algorithms to recognize signs and indicators of human trafficking activity, such as suspicious financial activity, unusual travel patterns, and even unique language in online advertisements. Ijiga et al. Explain that by automating detection, this type of investigation can improve both the speed and accuracy of identifying potential human trafficking cases, which can lead to faster interventions.

Other researchers have emphasized that anti-trafficking efforts should be carefully monitored to ensure that online investigations are not unduly influenced by stereotypes or biased algorithms.

Some have highlighted the need to examine the implementation of anti-trafficking technology through a cost-benefit analysis to, among other things, avoid misinterpreting information or not having a complete picture before jumping to conclusions.(ii)

Artificial support, human support

Ijiga et al. Recognize the need for collaboration between law enforcement and AI technology developers to maximize the positive impact of new investigative techniques. They conclude that deep learning applications, if well understood and used correctly, can be critical components in the fight against human trafficking. The combination of technology and human intervention is essential to support victims in real time and prepare them to enter a new life full of healthy relationships, happiness and hope.