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USA: Wrongfully arrested man sues US police over false facial-recognition match

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A US man has sued Detroit police after he was wrongfully arrested and falsely identified as a shoplifting suspect by the department’s facial recognition software, one of the first lawsuits of its kind to call into question the technology’s risk of throwing innocent people in jail.

Robert Williams, a 43-year-old father in the Detroit suburb of Farmington Hills, was wrongfully arrested last year and accused of taking watches from a Shinola luxury goods store after US police investigators used a facial recognition search of the stop’s surveillance-camera footage that identified him as the thief.

Prosecutors dropped the case less than two weeks later, saying officers had relied on insufficient evidence. Police Chief James Craig later apologised for what he called “shoddy” investigative work.

Williams, who said he had been driving home from work when the 2018 theft had occurred, was interrogated by detectives and held in custody for 30 hours before his release.

Williams’s case sparked a public outcry about the fast-growing use of a technology that research has shown often misidentifies people of colour. His lawsuit is at least the third brought by black men in the United States to raise doubts about the software’s accuracy.

The case could heighten the legal challenges for a technology that is largely unregulated in the US, even as it has become a prolific investigative tool used by US police forces and federal investigators. While the software has been banned by more than a dozen cities nationwide, it has been cited in a growing number of criminal cases, including in the landmark investigation of rioters at the Capitol on January 6.

Williams’s attorneys did not make him available for comment. But Williams wrote in a Washington Post opinion column last year that the episode had left him deeply shaken, in part because his young daughters had watched him get handcuffed in his driveway and put into a police car after returning home from work.

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