Ask what AI did, why it was used, what evidence a journalist checked, whether the use was disclosed, how sensitive material was protected, and who is responsible for the final story. The most useful answer explains the process—not just that “AI was used”—and gives readers a way to assess important claims.
What questions should readers ask about AI-assisted journalism?
Use these questions to evaluate a specific article or compare how different outlets handle AI. They apply to very different uses, from transcribing an interview to generating an image, so ask what happened in this story rather than treating all AI assistance as equivalent.
- What role did AI play? Was it used to transcribe, translate, extract data, help with research, draft or edit text, suggest a headline, create an image, or do something else?
- Why was AI used for that task? Did it help journalists work through a large collection of records, or take over a step that called for human judgment? The reason matters because the risks differ by task.
- Who checked the AI-assisted material, and what did they check? Did a journalist compare claims with original documents, sources, or other evidence? Who has the authority to correct the story?
- Can important claims be traced to evidence? For reporting based on data or documents, can readers inspect the underlying records or understand how the newsroom reached its conclusions?
- Did AI create or alter anything I am seeing or hearing? If an image, audio clip, or video is synthetic or manipulated, is that clearly identified and presented in context?
- Does the disclosure explain the process? Does it say what AI did, what a person reviewed, and who retains editorial responsibility?
- How did the newsroom protect sources and unpublished material? Were confidential documents, identities, or drafts entered into an external AI service? What safeguards applied?
- Who is accountable if something is wrong? Look for the newsroom or journalist responsible for the final work and a clear way to report an error.
What does a credible answer look like?
Specific use, not a vague label
A statement that AI was “used in production” leaves basic questions unanswered. A useful explanation identifies the task and its scope: for example, that a tool transcribed an interview, or helped extract entries from a document collection. Those uses are not the same as asking a system to draft a story or generate a visual.
The Associated Press advises journalists to consider whether AI was used, why it was chosen, how it was applied, and how its output was verified. It also describes AI as potentially useful for tasks such as debugging code or extracting structured data, while warning that errors can create additional verification work. AP’s account of AI and data journalism describes a project in which users could trace each data point back to its source.
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Verification against evidence
AI output can sound certain without being reliable. A strong account explains what a journalist checked and against what evidence. Reuters’ standards say that AI-generated facts, sources, and claims must be independently verified and fact-checked by Reuters journalists. Its guidance says not to trust unverified, unattributed AI-generated facts. Reuters’ journalistic standards also state that the organization remains responsible for the content it publishes when an AI tool is involved.
For a reader, “a human reviewed it” is not enough to judge the process. The reviewer needs access to the underlying evidence and authority to make editorial decisions. If a disclosure does not say what was checked, it may not answer the central question.
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Disclosure that explains oversight
A meaningful disclosure goes beyond an AI label. It should help readers understand the tool’s material role, the human review, and who stands behind the published work. The AP says it sets disclosure standards when generative AI plays a material role in published content. Its standards also call for clear identification and context when AI-generated or manipulated material is included. AP’s July 2026 standards update describes those expectations.
A Reuters Institute analysis argues for explaining the layers of authorship and oversight rather than treating a bare AI label as a complete account. The Reuters Institute for the Study of Journalism discusses why the authorial process and oversight matter to public understanding.
Protection for sources and unpublished work
Ask whether confidential information, source identities, unpublished drafts, or sensitive documents were submitted to an external AI platform and what protections were in place. The Thomson Reuters Foundation’s newsroom guide identifies these materials as sensitive and recommends that newsroom policies address their use with AI tools. The Thomson Reuters Foundation’s 2025 newsroom guide covers source and material protection.
Clear responsibility for errors
AI assistance does not by itself tell readers who is responsible for accuracy, fairness, or corrections. Reuters says responsibility remains with Reuters and its journalists; AP likewise says its journalists remain accountable for accuracy and fairness. These are examples of the policies of particular organizations, not a universal rule or proof of what another outlet did in a given story. For a specific article, look for the outlet’s own policy and a correction contact.
How do newsroom policies differ from what happened in one story?
A published policy describes an organization’s stated approach. It does not establish that a particular story followed every step. Conversely, a disclosure about one story may not describe the outlet’s general rules. To evaluate a piece, compare its disclosure with the outlet’s policy, then look for evidence that the important claims can be checked.
Visual standards are one area where policies may differ. Reuters prohibits generative AI from creating or enhancing its visual journalism, while permitting approved assistance that does not alter the underlying visuals. The AP’s July 2026 standards call for clear identification and context when AI-generated or manipulated material is included. These positions describe those organizations; readers should not assume every newsroom follows either policy.
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Why ask these questions?
In a 2025 Reuters Institute survey across Argentina, Denmark, France, Japan, the United Kingdom, and the United States, 12% of respondents said they 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 checked AI outputs before publication; the reported figures ranged from 25% in the UK to 42% in Japan and 44% in Argentina. These are public perceptions, not direct observations of every newsroom’s work.
The same survey found that 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 measure perceptions, not actual changes in news quality. The Reuters Institute’s 2025 Digital News Report gives the survey’s country scope and findings.
How to compare two AI-assisted stories
When comparing outlets or individual pieces, assess the same six points for each rather than relying on a general statement that one uses AI responsibly:
Quick Recap
- Role and extent: What did AI do, and how much of the work did it affect?
- Stakes: Did it affect a consequential claim, a sensitive source, or material readers rely on as evidence?
- Verification: Were claims checked independently against original records or other evidence?
- Disclosure: Does the explanation cover both AI’s role and human oversight?
- Source protection: Were confidential information and unpublished material handled with safeguards?
- Accountability: Is it clear who owns the final story and handles corrections?
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