CAIHL read · Jun 13, 2026

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States Move Ahead with AI Safeguards for Minors as Federal Action Remains Uncertain

What CAIHL does

Critical AI Health Literacy (CAIHL) is an analytical lens — Hugo Campos and Liz Salmi's 2025 National Academy of Medicine commentary, "Critical AI Health Literacy as Liberation Technology." It applies Paulo Freire's theory of critical literacy to health AI.

The central question CAIHL asks is whose interests does this AI actually serve? Four dimensions answer it: who is the primary user, where is it hosted, whose interests does it advance, and does it expand or constrain patient agency.

This deep-read separates the four dimensions on a single item from the day's scan, so you can see the specific structural shape of the AI in question — not just the bucket it landed in.

How this item reads through CAIHL

Primary user

patient

Patients, families, and care partners are the primary users of this AI.

Hosting

government

Hosted or controlled by a government agency or program.

Interests

patient-aligned

Interest structure prioritizes patients. Operates on a philanthropic, public-service, or advocacy footing.

Agency

expanding

Expands patient capabilities, supports their questions, increases their ability to act on their own values across and beyond health systems.

One-sentence synthesis

State-level legislative momentum on AI safeguards for minors; expanding agency through the sub-federal layer where federal action stalls.

How this item appeared in the daily scan

Editor's note: The federalism gap is the operational shape the patient-AI legislative pattern is taking. States are moving; federal is uncertain; the adult-patient surface is in the gap between them.

Summary: PYMNTS: Federal-versus-state framing of the AI-minors safeguard landscape — states (NY, CA, CO) moving ahead with concrete bills; federal floor unsettled. Maps the deployment pattern across the seven states with active legislation.

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methodology

Limitations

CAIHL is a lens, not a verdict. The four dimensions are conditions of use — reassess them when a tool's business model, deployment context, or patient behavior changes. See the NAM commentary for the full framework.