AI helps in the fight to spot sex traffickers
The University of Berkeley, California has developed a method of finding human traffickers using machine learning. The technique utilises AI to spot common patterns in ads for sex workers on sites such as Backpage, and tracks the people behind the ads from publicly available data on the payment method, Bitcoin. The only way to pay for an ad on Backpage is through Bitcoin, making the payment traceable through the blockchain to a particular wallet. The technology was tested on 10,000 ads with 90% accuracy, and tracked a wallet that was responsible for $150,000 worth of ads, likely indicating an exploitation racket.

Follow the money APA-PictureDesk GmbH/REX/Shutterstock

After Kubiiki Pride’s 13-year-old daughter disappeared, it took 270 days for her mother to find her. When she did, it was as an escort available to be rented out on an online classified web site. Her daughter had been drugged and beaten into compliance by a sex trafficker.

To find her, Pride had to trawl through hundreds of advertisements on Backpage.com, a site that in 2012, the last date for which stats are available, was hosting more than 70 per cent of the US market for online sex ads. When it comes to identifying signs of human trafficking in online sex adverts, the task for police is often no easier. Thousands of sex-related classifieds are posted every week. Some are legal posts. Other people, like Pride’s daughter, are forced to do it. Working out which ads involve foul play is a laborious task.

However, the task is being automated using a strange alliance of artificial intelligence and bitcoin.

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“The internet has facilitated a lot of methods that traffickers can take advantage of. They can easily reach big audiences and generate a lot of content without having to reveal themselves,” says Rebecca Portnoff at the University of California, Berkeley.

But a new tool developed by Portnoff and her colleagues can ferret traffickers out. It uses machine learning to spot common patterns in suspicious ads, and then uses publicly available information from the payment method used to pay for them – bitcoin – to help identify who placed them.

The digital trail

The tool will help not only the investigation and intervention of potential traffickers, “but also to support prosecution efforts in an arena where money moves with rapidity across financial instruments and disappears from the evidence trail”, says Carrie Pemberton Ford at the Cambridge Centre for Applied Research in Human Trafficking.

There are about 4.5 million people who have been forced into sexual exploitation. In the US, many of them end up advertised on Backpage, the second biggest classified ad listing site. People list everything from events to furniture there, but it has also become associated with sex ads and sex trafficking – so much so that the US National Center for Missing and Exploited Children has said that the majority of child sex trafficking cases referred to them involve ads on Backpage.

Normally, the tell-tale sign that an advert involves trafficking is that the person behind it is responsible for many other adverts across the site. However, this is difficult to spot, as adverts mention the people being trafficked, not the traffickers.

To identify the authors of online sex ads, Portnoff’s tool looks at the style in which ads are written. Artificial intelligence trained on thousands of different adverts highlights when similar styles have been used, and clusters together likely candidates for further investigation.

The second step comes via the payment method. Credit card companies stopped the use of their services on Backpage in 2015, leaving bitcoin as the only way to paying for adverts.

Every transaction made using bitcoin is logged on a publicly available ledger called the blockchain. It doesn’t store identities, but every user has an associated wallet that is recorded alongside the transaction. The AI tool searches the blockchain to identify the wallet that corresponds to each advert.

Evidence for the prosecution

It is also easy to see when each ad was posted. “We look at cost of the ad and the timestamp, then connect the ad to a specific person or group. This means the police then have a pretty good candidate for further investigation,” says Portnoff.

Once the police know which ads are of dubious origin, they can call the numbers on them in the knowledge that they might well be linked to crime. “Narrowing down from the hundreds of thousands of ads online will be very useful for law enforcement officers who have to read through so many ads during an investigation,” says Portnoff.

During a four-week period, the research team tried out their tool on 10,000 adverts. It correctly identified about 90 per cent of adverts that had the same author, with a false positive rate of only 1 per cent. One of the bitcoin wallets they tracked down was responsible for $150,000 worth of sex adverts, possible evidence of an exploitation ring.

Backpage has not yet responded to New Scientist’s requests for comment.

The team is working with a number of different police forces and NGOs with the hope of using the tool in real investigations soon. The work was presented at the Conference on Knowledge Discovery and Data Mining in Canada this month.

The trafficker who kidnapped Kubiiki Pride’s daughter was eventually caught and sentenced to five years in prison. Successfully prosecutions like that are rare, but with Portnoff’s new tool that could soon change.