In 2015, two undercover police officers in Jacksonville, Fla., bought $50 worth of crack cocaine from a man on the street. One of the cops surreptitiously snapped a cellphone photo of the man and sent it to a crime analyst, who ran the photo through facial recognition software.
The facial recognition algorithm produced several matches, and the analyst chose the first one: a mug shot of a man named Willie Allen Lynch. Lynch was convicted of selling drugs and sentenced to eight years in prison.
Civil liberties lawyers jumped on the case, flagging a litany of concerns to fight the conviction. Matches of other possible perpetrators generated by the tool were never disclosed to Lynch, hampering his ability to argue for his innocence. The use of the technology statewide had been poorly regulated and shrouded in secrecy.
But also, Willie Allen Lynch is a Black man.
Multiple studies have shown facial recognition technology makes more errors on Black faces. For mug shots in particular, researchers have found that algorithms generate the highest rates of false matches for African American, Asian and Indigenous people.
After more than two dozen police services, government agencies and private businesses across Canada recently admitted to testing the divisive facial recognition app Clearview AI, experts and advocates say it’s vital that lawmakers and politicians understand how the emerging technology could impact racialized citizens.
“Technologies have their bias as well,” said Nasma Ahmed, director of Toronto-based non-profit Digital Justice Lab, who is advocating for a pause on the use of facial recognition technology until proper oversight is established.
“If they don’t wake up, they’re just going to be on the wrong side of trying to fight this battle … because they didn’t realize how significant the threat or the danger of this technology is,” says Toronto-born Toni Morgan, managing director of the Center for Law, Innovation and Creativity at Northeastern University School of Law in Boston.
“It feels like Toronto is a little bit behind the curve in understanding the implications of what it means for law enforcement to access this technology.”
Last month, the Star revealed that officers at more than 20 police forces across Canada have used Clearview AI, a facial recognition tool that has been described as “dystopian” and “reckless” for its broad search powers. It relies on what the U.S. company has said is a database of three billion photos scraped from the web, including social media.
Almost all police forces that confirmed use of the tool said officers had accessed a free trial version without the knowledge or authorization of police leadership and have been told to stop; the RCMP is the only police service that has paid to access the technology.
Multiple forces say the tool was used by investigators within child exploitation units, but it was also used to probe lesser crimes, including in an auto theft investigation and by a Rexall employee seeking to stop shoplifters.
While a handful of American cities and states have moved to limit or outright ban police use of facial recognition technology, the response from Canadian lawmakers has been muted.
According to client data obtained by BuzzFeed News and shared exclusively with the Star, the Toronto Police Service was the most prolific user of Clearview AI in Canada. (Clearview AI has not responded to multiple requests for comment from the Star but told BuzzFeed there are “numerous inaccuracies” in the client data information, which they allege was “illegally obtained.”)
Toronto police ran more than 3,400 searches since October, according to the BuzzFeed data.
A Toronto police spokesperson has said officers were “informally testing” the technology, but said the force could not verify the Star’s data about officers’ use or “comment on it with any certainty.” Toronto police Chief Mark Saunders directed officers to stop using the tool after he became aware they were using it, and a review is underway.
But Toronto police are still using a different facial recognition tool, one made by NEC Corp. of America and purchased in 2018. The NEC facial recognition tool searches the Toronto police database of approximately 1.5 million mug shot photos.
The National Institute of Standards and Technology (NIST), a division of the U.S. Department of Commerce, has been testing the accuracy of facial recognition technology since 2002. Companies that sell the tools voluntarily submit their algorithms to be tested to NIST; government agencies sponsor the research to help inform policy.
In a report released in December that tested 189 algorithms from 99 developers, NIST found dramatic variations in accuracy across different demographic groups. For one type of matching, the team discovered the systems had error rates between 10 and 100 times higher for African American and Asian faces compared to images of white faces.
For the type of facial recognition matching most likely to be used by law enforcement, African American women had higher error rates.
“Law enforcement, they probably have one of the most difficult cases. Because if they miss someone … and that person commits a crime, they’re going to look bad. If they finger the wrong person, they’re going to look bad,” said Craig Watson, manager of the group that runs NIST’s testing program.
Clearview AI has not been tested by NIST. The company has claimed its tool is “100% accurate” in a report written by an “independent review panel.” The panel said it relied on the same methodology the American Civil Liberties Union used to assess a facial recognition algorithm sold by Amazon.
The American Civil Liberties Union slammed the report, calling the claim “misleading” and the tool “dystopian.”
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Clearview AI did not respond to a request for comment about its accuracy claims.
Before purchasing the NEC facial recognition technology, Toronto police conducted a privacy impact assessment. Asked if this examined potential racial bias within the NEC’s algorithms, spokesperson Meaghan Gray said in an email the contents of the report are not public.
But she said TPS “has not experienced racial or gender bias when utilizing the NEC Facial Recognition System.”
“While not a means of undisputable positive identification like fingerprint identification, this technology provides ‘potential candidates’ as investigative leads,” she said. “Consequently, one race or gender has not been disproportionally identified nor has the TPS made any false identifications.”
The revelations about Toronto police’s use of Clearview AI have coincided with the planned installation of additional CCTV cameras in communities across the city, including in the Jane Street and Finch Avenue West area. The provincially funded additional cameras come after the Toronto police board approved increasing the number placed around the city.
The combination of facial recognition technology and additional CCTV cameras in a neighbourhood home to many racialized Torontonians is a “recipe for disaster,” said Sam Tecle, a community worker with Jane and Finch’s Success Beyond Limits youth support program.
“One technology feeds the other,” Tecle said. “Together, I don’t know how that doesn’t result in surveillance — more intensified surveillance — of Black and racialized folks.”
Tecle said the plan to install more cameras was asking for a lot of trust from a community that already has a fraught relationship with the police. That’s in large part due to the legacy of carding, he said — when police stop, question and document people not suspected of a crime, a practice that disproportionately impacts Black and brown men.
“This is just a digital form of doing the same thing,” Tecle told the Star. “If we’re misrecognized and misidentified through these facial recognition algorithms, then I’m very apprehensive about them using any kind of facial recognition software.”
Others pointed out that false positives — incorrect matches — could have particularly grave consequences in the context of police use of force: Black people are “grossly over-represented” in cases where Toronto police used force, according to a 2018 report by the Ontario Human Rights Commission.
Saunders has said residents in high-crime areas have repeatedly asked for more CCTV cameras in public spaces. At last month’s Toronto police board meeting, Mayor John Tory passed a motion requiring that police engage in a public community consultation process before installing more cameras.
Gray said many residents and business owners want increased safety measures, and this feedback alongside an analysis of crime trends led the force to identify “selected areas that are most susceptible to firearm-related offences.”
“The cameras are not used for surveillance. The cameras will be used for investigation purposes, post-reported offences or incidents, to help identify potential suspects, and if needed during major events to aid in public safety,” Gray said.
Akwasi Owusu-Bempah, an assistant professor of criminology at the University of Toronto, said when cameras are placed in neighbourhoods with high proportions of racialized people, then used in tandem with facial recognition technology, “it could be problematic, because of false positives and false negatives.”
“What this gets at is the need for continued discussion, debate, and certainly oversight,” Owusu-Bempah said.