Power In Numbers
Crowdsourcing apps help fight human trafficking
Location-based apps are also emerging as a way to leverage technology against human trafficking—in particular apps that harness the power of the crowd. TraffickCam, introduced in 2015 by the Exchange Initiative, enables the public to help fight sex trafficking by uploading photos of hotel rooms while traveling.
Sex traffickers regularly post photos of their victims in hotel rooms, therefore these photos could be potential evidence used to locate and prosecute perpetrators. However, the location element is essential. The goal of TraffickCam is to create a database of hotel room images investigators can efficiently search to find other images taken in the same location.
Another app, See | Say, allows the public to anonymously report labor or sexual exploitation of adults or minors in their communities. Data collected through the app is then reported to law enforcement agencies to assist with investigations. See | Say was developed by Diginido Labs as a result of input from DeliverFund, a nonprofit that applies intelligence to human trafficking investigations. Diginido and DeliverFund connected in April at a human trafficking-focused hacakthon hosted by ATHack! with Microsoft Reactor.
However, some law enforcement officials are concerned about the veracity of crowdsourced information—asking what’s to, for example, stop traffickers from taking pictures in Atlanta hotel rooms and uploading them to the TraffickCam app claiming Chicago as the location.
“Law enforcement certainly has questions and concerns,” said 2nd Lt. James Bacon, who oversees the Child Exploitation Squad for the Fairfax County Police Department in Virginia. “Who is vetting that information?”
A spokesperson for The Exchange Initiative clarified that the TraffickCam app uses GPS verification to confirm the authenticity of the user’s location and prevent attempts to manipulate the data.
Ehb Teng, co-founder of ATHAck! and Diginido Labs, said the crowd should drown out any efforts by traffickers to skew data. “If [traffickers] generate any false positives—even if coordinated in incredibly small clusters compared to larger clusters at the public level—you can intelligently disseminate between those false positives and the actual cases that need investigated over the longer term,” Teng said.
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