AI against human trafficking
A game changer in the fight against modern slavery.
There are 40.3 million victims of human trafficking globally, according to the International Labor Organization.
This ruthless exploitation of our fellow humans drives a $150 billion-per-year criminal industry — in the same league with drug trafficking and major financial crimes. Profits are often so lucrative that in the U.S., a trafficked teen can fetch the pimp as much as six figures a year. Human trafficking victims are forced into agricultural, domestic and industrial labor.
According to IBM,
There are three approaches to help thwart these crimes:
NGOs help communities become more aware of trafficking and to support victims.
Law enforcement targets the traffickers.
Financial institutions identify and track the flow of funds related to trafficking.
Of these three approaches, helping banks follow the money is where technology is making a major impact
Tracking transactions in flagged bank accounts, and combining that with data from multiple sources can help in tracking down major trafficking circuits and could result in wide scale takedowns of notorious organisations.
Traditionally, finding a missing person would involve taping a picture of the person on the computer and then manually combing through thousands, if not millions, of online ads on adult services websites to see if any of the posted pictures match. Such a process is time-consuming and tiring.
AI takes on this sifting task effectively. It combs through millions of online ads in specialty “hot spots” (commercial adult services websites) and searches for “vulnerability indicators.” These can include images of subjects who look like children and indications of drug use.
AI is useful in handling unstructured data, which is not presented in familiar digital formats. Computer vision also helps analyse images and scan for potential problems. Importantly, AI can do such work at scale. Working at scale is necessary with estimates stating that 37% of all online content is pornographic material.
Stanford Medicine statistician and assistant professor of epidemiology Mike Baiocchi is working on a decision-support tool for the lab, which they call the “intuition engine.” The architecture is based on a data processing pipeline that transforms a constant flow of incoming trafficking clues from a variety of sources — and then comes up with predictions of trafficking risks.
“We’re trying to make give prosecutors the kinds of information they need so they can tackle these complicated cases” said Baiocchi.
Demi Moore and Ashton Kutcher lent their celebrity status to this effort by founding Thorn, a tech-oriented organisation dedicated to defending children from sexual exploitation. Their flagship program, Spotlight, uses Amazon’s proprietary facial recognition technology to help track missing children, in part by matching them with online sex ads on the dark web. According to Thorn, Spotlight has been used to identify about 10 juvenile victims per day and over 16,000 traffickers in total since its launch in 2014.
However, one of the biggest drawbacks of the facial recognition technology is the fact that it is found to be biased against women of colour. This is a real issue with most trained models that are used widely, even with the advent of synthetic data, organisations continue to train models on biased datasets, which only reduces the accuracy of their technology, and in such high impact applications, where women of colour are the biggest victims.
A study found that several commercial facial analysis systems falsely identified dark-skinned females more than any other group, with an error rate of 35.7%, compared to 0.8% for lighter-skinned males. This may be due to a strong bias in the types of faces that these technologies have been trained on historically.
Another drawback of this technology is that it could be used to target consenting sex workers and surveillance of people which would be a breach of privacy.
The Brick Belt, an area of land extending across Pakistan, northern India, Nepal and Bangladesh, is plagued by extreme labor exploitation among its estimated 55,387 brick kilns.
A way to combat it would be to attack the money supply to these systems and that can be done by informing the clients of organisations that exploit people of their misdoing.The project of anti-trafficking group Made in A Free World, helps its corporate clients stay up-to-date on real-time risk developments in their supply chains. They do this, in part, by employing machine learning “crawlers” to scour the media for relevant news stories about labor abuses.
A variety of data sources are really useful in generating insights as to the causality of people falling into such traps where they fall victim to human trafficking in search of work opportunities. Research in countries like Tanzania and Bangladesh using data from satellites, drones and mobile money transfer apps and compensation of workers are pretty good indicators of presence of trafficking and forced labour.
Artificial intelligence promises to be a powerful tool in the global fight against various forms of human trafficking.With the power of scaling, machine learning could help activists, governments, and corporations more effectively tackle this ever-evolving issue. But AI itself is not a remedy, and its real-world implementation may give rise to a variety of ethical and practical problems. As this technology becomes more accessible, anti-trafficking efforts should remain on alert for the serious bias and privacy concerns that come with smarter tools.