Man Arrested For Sock Theft by False Facial Match Despite Alibi

This report describes widespread use of facial recognition technology by police departments across the United States, with specific focus on cases in New York and Florida. The technology was used to identify criminal suspects by comparing surveillance photos to databases of mugshots and driver's license photos. Key incidents include a Bronx case where Kaitlin Jackson's client was charged with stealing socks from TJ Maxx after being identified through facial recognition, despite having an alibi that he was at a hospital during his son's birth. The client spent six months in jail before accepting a plea deal. Another case involved Larry Griffin II, who was identified and arrested within hours after placing fake bombs in New York subway stations using facial recognition analysis of surveillance footage. The report reveals that facial recognition searches often lead to suggestive identification procedures, with police showing witnesses single photos generated by the system rather than conducting proper lineups. Studies show the technology has higher error rates for people with dark skin, women, and young people, with even the best algorithms being wrong more than 20 percent of the time. The FBI's facial recognition database includes over 641 million images of American adults, and the technology is deployed without disclosure requirements in most jurisdictions.