States Deploy AI for Benefits Screening With Few Safeguards
Medicaid, SNAP, and unemployment programs increasingly rely on algorithms that advocates say lack transparency and proper oversight.
State governments are rapidly integrating artificial intelligence into social safety net programs, using the technology to screen applicants for Medicaid, food assistance, and unemployment benefits—often without adequate safeguards to prevent errors that could strip eligible Americans of essential support.
Why it matters
AI systems can produce inaccurate results, and when those errors affect benefit eligibility determinations, vulnerable families may lose access to healthcare, food assistance, or income support they legally qualify for. Without transparency requirements or rigorous testing protocols, states risk automating harm at scale.
Federal policy drives AI adoption
President Trump's One Big, Beautiful Bill has accelerated the shift toward AI-powered benefit administration, according to Allison Buffett, senior health policy analyst at the Bipartisan Policy Center. The legislation introduces stricter work requirements and more frequent eligibility recertifications for programs like Medicaid and SNAP starting next year, creating administrative burdens that states are offloading to automated systems rather than human caseworkers.
Florida's 2027 budget includes funding for an AI system to verify SNAP eligibility. New Hampshire is partnering with Google Gemini to streamline unemployment claim submissions. Several states have launched AI chatbots to field Medicaid eligibility questions, Axios reports.
Track record raises concerns
Kevin De Liban, founder of TechTonic Justice, successfully sued Arkansas over an algorithm the state used to calculate Medicaid home-based care benefits. The system reduced eligible hours for thousands of residents in what De Liban describes as a "haphazard and harmful" manner.
During litigation, De Liban discovered Arkansas had not projected the magnitude of benefit cuts before deployment and employed no staff members who could explain the algorithm's mechanics until more than 18 months after implementation. After reverse-engineering the system himself, he found it operated in "patently absurd ways" disconnected from actual care needs.
The case illustrates a broader problem: AI vendors often claim their algorithms are proprietary, refusing to explain how their systems reach decisions. This opacity can leave wrongly denied applicants without benefits for months, even after they prove eligibility.
What experts recommend
Amanda Renteria, CEO of Code for America, emphasizes the need for continuous testing and monitoring. She recommends piloting AI systems in small counties before statewide rollout and watching for sudden drops in eligibility rates that might signal algorithmic errors.
Buffett warns that wrongful denials can be "devastating" for families, cutting off access to healthcare and services that enable people to remain in their communities and maintain productive lives.
De Liban acknowledges governments face pressure to improve efficiency but argues that "unacceptable" harms to vulnerable populations require a different approach. "State officials either need to vet these technologies appropriately, or they need to not use them and use a lower-tech way of administering their programs," he said.
These details were first reported by Axios.
This is an original analysis by the Omega editorial team. Source reporting: AI Watch.
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