A source-to-scene map is a working ledger that ties every factual statement in an AI-assisted explainer to the source that supports it and to the exact scene, narration line, chart, caption or on-screen text where the statement appears. It is the most practical way to show that an explainer’s facts can be checked, and it catches the most common failure in AI-assisted explainers: a visual or caption that takes a claim further than its source goes.
Why a scene-level ledger works better than a source list
A closing list of sources at the end of a video or article tells viewers where information came from, but it does not say which sentence rests on which document. In an explainer, claims also appear in places a bibliography never covers: a lower-third caption, a bar chart’s axis label, a diagram arrow, or a number read aloud over b-roll. A scene-level ledger treats each of those as a claim that needs its own line, so a reviewer can check the exact wording against the exact passage.
How to build the map
- Break the script into scenes or beats. Number them in the order viewers see them. Keep narration, captions, charts and graphics in the same scene when they appear together.
- List every factual claim in each scene. Include dates, figures, named laws or products, causal statements (“this caused”), and comparisons (“faster than”). Do not skip claims that appear only in a graphic.
- Record each claim in the ledger. Use the fields in the table below. Copy the claim word for word as it appears on screen or in the script, because a paraphrase can quietly change its meaning.
- Classify the claim. Mark it as directly stated by a source, as a calculation you performed from source numbers, or as an editorial inference. Only the first category can be checked by matching wording.
- Run a human review against the cited material. A reviewer opens the source, finds the passage, confirms the claim matches, and confirms the visual does not imply more than the passage supports.
- Re-open the ledger whenever the script or a visual changes. A new chart, a reworded caption or a new scene transition can change what a claim says, even when the source stays the same.
What each ledger entry should contain
| Field | What to record | Why it matters |
|---|---|---|
| Scene and asset | Scene number, and whether the claim sits in narration, a caption, a chart or a graphic | Lets a reviewer find the claim again after edits |
| Claim wording | The exact sentence, number or label as shown or spoken | Prevents a checked paraphrase from replacing the published wording |
| Source title and URL | Publisher, document title, and a working link to the page or file | Makes the claim traceable to a location outside the newsroom |
| Relevant passage or data | The quoted sentence, table row, or dataset field that supports the claim | Shows what the source actually says, not what it is generally taken to mean |
| Source date | Publication or last-update date of the source, and the date you checked it | Old guidance and superseded figures are a common source of errors |
| Claim type | Directly stated, calculation, or editorial inference | Tells the reviewer what kind of check is needed |
| Review status | Reviewer name or initials, date, and result (confirmed, reworded, or removed) | Creates a record that the check happened |
Handling revisions
Most traceability failures happen after a claim was first checked. Treat the following changes as triggers for a fresh check of the affected rows:
- Any change to a number, date, name or unit, even a rounding change.
- A new or reordered scene that moves a claim next to a chart or image it did not previously sit beside.
- A caption or label added to b-roll, since captions are read as factual statements.
- A source update, such as a revised guidance document or a new data release.
Keep provenance signals separate from fact-checking
Provenance tools answer a different question from a fact-check. They can suggest where a file came from, but they cannot tell you whether a statement in it is true. Google’s guidance for AI-generated web content states: “It is critical to manually factcheck and review all AI-generated content for accuracy and trustworthiness before publishing.” Google also suggests sharing how content was created, including context about automation, in a way that makes sense for your audience. Those two practices complement each other; neither replaces the other.
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The limits of the most commonly cited tools are specific:
- OpenAI’s Content Provenance API checks supported images and audio for specific OpenAI signals. OpenAI’s API documentation states: “The API checks for supported OpenAI signals. It isn’t a general-purpose AI detector and doesn’t identify content generated by every AI system.” A missing signal does not prove that content was made without AI.
- OpenAI’s text watermark can indicate that an OpenAI system generated or processed part of a passage. It does not measure how much a human contributed, and it does not establish ownership or factual accuracy.
Text watermark detection is also sensitive to editing. OpenAI’s own text watermark evaluations, published in 2026, were run in its own system and an example domain, not across the open web. At a target false-positive rate of 1%, they reported about 80% detection for 200-token passages and about 95% for 400-token passages. Replacing 10% of words in 400-token passages reduced detection from about 92% to 66%, and replacing 25% reduced it to about 17%.
| Condition (OpenAI text watermark evaluation, 2026, example domain) | Reported detection rate |
|---|---|
| 400-token passage, unedited, 1% false-positive target | about 92% in the 10% and 25% comparisons baseline; about 95% in the length comparison |
| 400-token passage, 10% of words replaced | about 66% |
| 400-token passage, 25% of words replaced | about 17% |
| 200-token passage, unedited, 1% false-positive target | about 80% |
These figures describe one vendor’s reported evaluation. They say nothing about how well editorial fact-checking works, and they should not be used to decide whether a script is accurate.
Disclosure and the EU transparency rules
The European Commission says that the transparency obligations in Article 50 of the EU AI Act apply from 2 August 2026, so they are already in force as of this article’s date. Whether they reach a particular publisher depends on that publisher’s role and content, and this article does not provide legal advice on that question. Publishers should check the current text and their own position.
The Commission’s accompanying code is voluntary, but the underlying Article 50 duties are legal obligations. The code describes provider marking and detection duties, and deployer labelling duties for specified content. It also states that deployer disclosure for AI-generated or manipulated public-interest text does not apply when the publication has undergone human review and is subject to editorial responsibility. Your ledger’s review column is the kind of record that makes that human-review condition easy to show, if it applies to you.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Checklist for judging any traceability process
Use these five questions to compare an editorial workflow, a spreadsheet template or a software tool:
- Claim-level linkage: can every factual statement be tied to a source and a publication location?
- Revision handling: does a changed scene trigger a review of its sources and claims?
- Evidence detail: can the reviewer keep the passage, dataset or calculation behind each claim?
- Provenance versus accuracy: does the process keep origin signals separate from factual verification?
- Reader context: can the team explain AI use and sourcing without implying that a provenance signal proves correctness?
These criteria follow from the goals of traceability and the limits described in the official guidance above. They are an editorial checklist, not a rating system, and no published study measures how much source-to-scene mapping improves accuracy. No official body prescribes a particular template, so the table above is a method you can adapt, not a required format.
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