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What questions should you ask about an AI-assisted story?
- What role did AI play? Ask whether it was used for transcription, translation, data extraction, research assistance, drafting, editing, headline suggestions, image creation, or another task. Those uses are not interchangeable: the risks and the checks needed depend on what the tool did.
- Why was AI used for that task? It may help journalists work through a large document collection or extract structured data, for example. But using it for a task that depends on a journalist’s judgment raises a different question: what did the tool contribute that a person then assessed?
- Who checked the output, and against what? Ask whether a journalist or editor compared the AI-assisted material with original records, sources, or other evidence. “A human reviewed it” is not very informative unless the reviewer could inspect the underlying evidence and had authority to challenge or change the result.
- Can important claims be traced to their sources? For a story based on data or documents, look for links to original records or an explanation of how the newsroom reached its conclusions. In an Associated Press account of a Big Local News project, readers could trace each data point back to its source.
- Did AI create or alter anything I can see or hear? Ask whether synthetic or manipulated images, audio, or video are clearly identified and put in context. Standards vary: AP’s July 2026 standards call for clear identification and context when such material is included, while Reuters prohibits generative AI from creating or enhancing its visual journalism and allows approved assistance that does not alter the underlying visuals. These are policies of specific organizations, not a universal rule.
- Does the disclosure explain the process? A useful disclosure says what AI did, what a person reviewed, and who retains editorial responsibility. A bare label may not tell readers whether AI helped with a minor production task or shaped material claims.
- How were sources and unpublished material protected? Ask whether confidential information, source identities, unpublished drafts, or documents were entered into an external AI platform, and what safeguards applied. The Thomson Reuters Foundation’s newsroom guide identifies these as sensitive materials that newsroom policy should address.
- Who is accountable if something is wrong? Look for the journalist, editor, or newsroom responsible for the published work and its corrections. A tool’s involvement does not explain who will answer for an error.
What does a credible answer look like?
Look for a concrete account of the task, the evidence used to verify the result, and the person or newsroom responsible. The Associated Press says AI output should be treated as unvetted source material and that journalists remain responsible for accuracy and fairness. Reuters requires its journalists to independently verify and fact-check AI-generated facts, sources, and claims, and says the organization remains responsible for what it publishes. Those published standards describe those newsrooms; they do not establish what every outlet does.
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Verification should be proportionate to the stakes. A transcription error in a low-stakes passage and an unsupported claim about a person or public institution do not carry the same consequences. In either case, confidence or fluency in the AI output is not evidence that it is correct. The important question is whether someone checked the material against evidence they could inspect.
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When comparing stories or outlets, consider the kind of AI use, the importance of the material it affected, the quality of independent checking, the detail in the disclosure, protection of confidential material, and clarity about corrections. These criteria help distinguish a clear, accountable process from a general assurance that a person was “in the loop.”
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Why do disclosure and oversight matter to readers?
A 2025 Reuters Institute for the Study of Journalism survey across Argentina, Denmark, France, Japan, the UK, and the US found that 12% of respondents were comfortable with news made entirely by AI, compared with 21% when there was some human oversight. On average across those six countries, 33% thought journalists always or often check AI outputs before publication; the figure ranged from 25% in the UK to 42% in Japan and 44% in Argentina. These are opinions, not observations of how every newsroom works.
The same survey found respondents expected generative AI to make news less trustworthy (net score −19) and less transparent (−8), while expecting it to make news cheaper to produce (+39) and more up to date (+22). These net scores describe perceptions, not measured changes in news quality. The findings help explain why a disclosure should describe a story’s process rather than ask readers to infer it from a label.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you assess an outlet’s answer?
- Specificity: Does it name the task AI performed, rather than treating all AI use as one thing?
- Evidence: Does it explain what was checked and against which records, sources, or other material?
- Meaningful oversight: Could the human reviewer assess the underlying evidence and make editorial decisions?
- Transparency: Does the disclosure describe AI’s role and human review when AI materially shaped published content?
- Safeguards: Does the account address the handling of confidential sources and unpublished information?
- Accountability: Is it clear who is responsible for the story and for correcting mistakes?
If the story does not answer these questions, you can ask the newsroom directly: “How was AI used in this story, what did a journalist verify against original evidence, and who is responsible for corrections?” That asks for the information a reader needs without assuming that AI was used badly—or that a human check was sufficient.
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