If privacy concerns you in this age of data collection, you might want to exit social media and scrub the digital breadcrumbs left by your accounts on Facebook, Snapchat, LinkedIn, Reddit, and other sites. Now, law enforcement is likely to create an enormous facial recognition database thanks to the ubiquitous online store Amazon that is selling the technology to police.
Law enforcement now employs people who can crunch "Big Data" across various platforms, including cell phone location records, credit card information (think airline tickets and the geo-stamp of gasoline purchases), friends groups, and license plate data are to create algorithms that determine the likelihood that an individual participated in a crime. On the flip side, there people hoping our scant privacy protections are upheld, although the Supreme Court has ruled that the Fourth Amendment does not ensure privacy when one is in public, making our faces and license plates fair game, despite the ACLU's protests.
Combined with mugshots of known criminals, a law enforcement facial recognition database is likely to be supplemented by police body camera footage, databases of driver's licenses, passports, and other identification photos such as professional licenses. And that likely can be combined with the scandalous Cambridge Analytica grab of data from more than 50 million Facebook users, which was used to push "fake news" that influenced voters in the American presidential election in 2016 and the United Kingdom's Brexit vote.
Facial recognition software will eventually be able to scan photos of crowds (taken by public surveillance cameras) and match faces to social media accounts, allowing anyone skilled in manipulating the technology the ability to track an individual's whereabouts, including workplace, friends, and shopping habits. (The technology "maps" a person's face for ultra-accurate comparisons, using measurements of facial features.)
Just like advertisers who pay for data about online browsing habits, police organizations can cobble together collected data points to develop a three-dimensional profile of a suspect and match it to known criminals. Hartford, Connecticut, where police just spent millions on drones, is hoping this "predictive policing" will help them catch bad guys before they are able to commit crimes, including by monitoring traffic at specific addresses (of known suspects) and patterns of behavior.
Pulling Data Together
In practical use, many large metropolitan police departments have successfully integrated a number of data points to assist policing. Called the Domain Awareness System, New York allows police to use smartphones to pull information collected by "shot-spotter" gunshot detectors as well as databases of mugshots that could be scanned to catch a suspect fleeing the area, or to use a license plate detection system to determine which vehicles were in the area at the time of the shooting. In New York, a variety of sensors sprinkled around the city monitor chemical and radiation releases.
Police regularly create "groups" of people from this data, including identifying individuals with similar tattoos that can signify gang affiliation. Put into use, that often means heavy policing of small areas populated by minorities, like Los Angeles' use of Palantir technology for "predictive policing" that has social justice groups up in arms.
This combination of voluntary and required data already helped police crack a significant string of rapes and murders in California that happened over a period of decades: the rapist's profile, culled from victims' descriptions, was augmented by DNA information available on an ancestry database that was submitted by a relative and clues stitched together by a discarded personal item that police tested for a DNA match.
On the cutting edge of the technology, police in the UK reconstructed fingerprint data from a social media photo of a man's hand holding illicit drugs, matched it with a known suspect and arrested him. Others are analyzing data input and output to track down those responsible for cyber crimes such as vacuuming up stolen credit card numbers or launching widespread phishing attacks.
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