It sifts through a database of hotel room pictures to match with online ads
Scientists are hoping artificial intelligence can help identify and rescue child victims of sex trafficking via an app and more than a million crowdsourced pictures of hotel rooms.
Researchers launched an app in 2016 to collect photographs of 50,000 hotels around the world that could be matched up with online advertisements placed by traffickers, who often use selfies taken by their victims in hotel rooms.
Hotels-50K can be used to identify where trafficking victims are being held — and ultimately to rescue them, according to a paper written by a group of scientists and presented at a conference on AI in Hawaii.
A huge number of pictures are put through an artificial intelligence engine called a deep convolutional neural network.
The engine “learns a set of filters,” said Abby Stylianou, co-author of the paper. “Here’s what a headboard looks like if it’s from this hotel. Here’s what a lamp looks like in that hotel.”
Signs of trouble
The initiative comes as hotels around the world are making efforts to stop sexual slavery, teaching employees signs to look for, including frequent sheet changes and “do not disturb” signs that are never removed.
There are 4.5 million people in sexual slavery around the world, according to the International Labor Organization.
The idea is to be able to pick out the specific hotel room and use the information to attempt a rescue.
By getting 150,000 people all over the world to download their app, TraffickCam, and take hundreds of thousands of pictures, it is easier to get the same view that a trafficking victim’s selfie taken for an ad is likely to have.
Once Hotels-50K was ready for testing about eight months ago, the scientists shared it with the National Center for Missing and Exploited Children (NCMEC), a U.S. non-profit involved in the fight against child sex trafficking.
It is unclear whether anyone has been saved directly because of Hotels-50K. “We’ve had a couple (of ads) that have given us leads and information,” Staca Shehan, executive director of the case analysis division at NCMEC, said.
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