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You generally cannot tell whether an unlabeled news story was written by AI just by reading its style. To judge how it was made, check the outlet’s AI disclosure and policy, who is responsible for the story, and whether its claims can be traced to sources. Those clues help you assess transparency and accountability; they are not proof of who wrote any particular passage.
What “AI-written” can mean
AI use in a newsroom spans very different tasks. A journalist might use a tool to transcribe an interview, translate text, summarize documents, or check spelling. In another workflow, AI might draft most of the article and a person might review it. Calling both simply “AI-written” obscures who led production and what the system contributed.
When comparing stories, ask what AI did and how central that contribution was: Was it support for a journalist, substantial text generation, or creation or alteration of an image? A useful disclosure names the task rather than relying on a broad AI label.
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How to assess an article’s production and accountability
Look for a specific disclosure
Check whether the outlet explains if AI drafted text, generated an image, summarized material, translated or transcribed it, or assisted with editing. Specific wording helps you understand the process. A vague label may signal AI involvement without explaining its scope.
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Identify the responsible people
Look for a byline and, where relevant, an editor or other named person responsible for verification. Then check whether the outlet describes who reviewed AI output. A byline or policy statement is an accountability signal, not independent proof that a particular passage was written or checked as described.
Trace consequential claims to evidence
Follow links to documents, data, named sources, and other evidence behind important claims. This lets you evaluate a story’s support whether or not AI was involved; visible sourcing does not reveal who composed the prose.
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Check image labels and context
Ask whether an image is documentary photography or generated or altered illustration, and whether the story says so. A generated image should not be mistaken for evidence that an event happened as depicted.
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Find the correction channel and newsroom policy
See whether the outlet offers a way to report errors and publishes an accessible policy explaining where AI is used and how its output is reviewed. These practices can make accountability clearer, but they cannot guarantee that a story is error-free.
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Can you recognize AI writing by its style?
The sources cited here do not establish a reliable style-based way to identify AI authorship in an individual, unlabeled story. A reader’s impression that prose sounds formulaic or polished is not a verified authorship test. Treat style-based AI detectors or intuitions as unverified unless evidence independently supports their reliability for the specific task.
For a practical comparison, focus on process rather than trying to diagnose the prose: who led production, what AI contributed, what the outlet disclosed, who is accountable, and whether readers can check the claims.
What surveyed readers say about AI and news
Reuters Institute for the Study of Journalism reporting provides a bounded view of audience attitudes, not a measurement of newsroom practice or a ranking of outlets.
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| Finding | What it describes |
|---|---|
| 12% comfortable with fully AI-generated news; 62% comfortable with entirely human-made content | Reuters Institute’s 2025 survey across Argentina, Denmark, France, Japan, the United Kingdom, and the United States. Comfort with AI increases when human journalists lead and AI assists, though views vary by country and task. Source: Reuters Institute, Digital News Report 2025. |
| 33% believed journalists always or often check AI outputs before publication | Respondents’ belief in the same 2025 six-country survey, not an audit of actual newsroom checks. Source: Reuters Institute, Digital News Report 2025. |
| 19% said they saw newsroom AI labels daily; 28% weekly; 77% said they consumed news daily | Self-reported frequency in the same surveyed markets, not direct observation of label use. Source: Reuters Institute, Digital News Report 2025. |
| 47% wanted disclosure when AI wrote article text; 32% when AI edited spelling and grammar | Reuters Institute’s 2024 reporting, showing that disclosure preferences vary with the task. These are not 2025 figures. Source: Reuters Institute, Digital News Report 2024. |
The 2024 report also found support for disclosure in other substantial uses, including data analysis and creating an image when a real photograph was unavailable. Its findings, like the 2025 results, reflect surveyed markets and differ across countries and age groups; they should not be generalized to every reader.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How one newsroom describes its rules
The Associated Press’s updated standards, announced July 23, 2026, distinguish assistance from material contributions. AP lists early-stage research and document summarization, transcription and translation, headline and summary suggestions, and grammar, spelling, and search optimization as allowed support tasks; its announcement says journalists review and edit AI-generated output. AP also says it prohibits generative AI from creating, altering, or enhancing news photography and requires disclosure when generative AI plays a material role in published content. These are AP’s organizational rules, not universal newsroom standards. Read AP’s announcement.
“Editorial judgment, verification and accountability remain the responsibility of AP journalists.”
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