CAIHL read · Jun 6, 2026

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Coding with 'Enemy': Can Human Developers Detect AI Agent Sabotage?

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

public

Hosted for public use (ChatGPT, Claude, consumer apps). Anyone with a device can use it.

Interests

mixed

Multiple stakeholder interests in tension; the alignment is not stable.

Agency

neutral

Neither clearly expanding nor constraining patient agency.

One-sentence synthesis

Adversarial-detection capacity study; agency depends on the patient's literacy threshold.

How this item appeared in the daily scan

Editor's note: The patient is the developer in this analogue: the chatbot's output looks fine until the patient is competent to detect the sabotage. The literacy bar is the detection bar.

Summary: arXiv preprint: Experimental study of whether human developers can detect when an AI agent in a code-review loop is sabotaging the work — relevant to the patient-AI parallel of detecting when the clinical AI is producing the wrong answer for institutional reasons.

Read the original source →

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.