Treat every factual claim in AI-generated text, images, or video as unverified until you can support it with reliable evidence. Check each claim separately, trace important claims to original records, and preserve dates and context. If a material claim remains uncertain, qualify it—or do not share it as fact.
1. Break the output into checkable claims
Do not decide that an entire AI response is either true or false. Separate its factual assertions from opinions, predictions, and rhetorical framing. Split compound sentences: one accurate detail does not validate every other detail in the same sentence.
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This claim-by-claim approach is also used in a 2025 preprint examining AI-generated news reports, which describes extracting “atomic” claims for assessment. Its findings concern experimental model assessment, not every model or fact-checking task. Read the study.
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Look for evidence closest to the original event or data: an official record, original research paper, government data release, transcript, recording, or primary document. The OSCE’s 2026 guide search result recommends sources such as official statistical agencies, peer-reviewed research, and reports from international organizations. Its search result also describes a quotation error that arose from relying on another outlet’s account instead of checking the original interview. See the OSCE guide.
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A search result, summary, or AI-generated explanation can point you toward evidence, but it is not a substitute for inspecting that evidence. When the claim matters, follow the trail back to the record itself.
3. Check whether the evidence matches the claim
A source can be genuine and still fail to support the statement being shared. Compare the claim with the evidence across these dimensions:
- Who and where: Is it about the same person, organization, place, or population?
- When: Does the evidence cover the same time period as the claim?
- Definition: Are key terms, categories, or measurements being used in the same way?
- Scope: Does the evidence support the full claim, or only a narrower part of it?
- Causation: Does the source establish that one thing caused another, or merely show that they occurred together?
For a quotation, compare the wording with the original and read the surrounding context. For a statistic, retain the number’s denominator, period, and publishing organization; a figure without those details can create a misleading impression.
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Model memory may not reflect recent events. For claims about current officeholders, prices, policies, evolving news, or local incidents, find evidence that is current and relevant to the stated place and date.
A 2025 preprint found that the models it studied assessed static claims better than dynamic claims, and national or international stories better than local ones. These are findings from that study, not a universal performance rating. They are a reason to be especially careful with fast-changing and local claims, where human review may be needed. Study details.
5. Corroborate consequential claims independently
For a claim with meaningful consequences, look for another reliable source that independently supports it and points to evidence. Several sites repeating the same unattributed account do not amount to independent confirmation. Compare sources by their proximity to the original evidence, accountability, publication date, geographic and definitional fit, and whether they corroborate one another.
Automated retrieval can help surface material, but relevance and quality matter. The 2025 study reports that retrieved evidence could reduce claims left unassessable while also increasing wrong assessments when results were irrelevant or low quality. Read the evidence, rather than counting search results or accepting a tool’s verdict. Study details.
6. Do not confuse detection with verification
AI detectors try to assess whether content was machine-generated; they do not establish whether a factual claim is true. Their results should not be treated as a truth test.
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NIST reports that, in its first text-summarization pilot, summaries from three generators fooled every detector evaluated. That is a bounded result from one pilot, not a general accuracy rate for all detectors or all types of content. NIST’s Generative AI evaluation program describes the evaluation.
7. For images and video, check provenance—but know what it means
When available, Content Credentials can record information about a piece of media’s origin, edits, and AI use. They can also show whether credentialed provenance is valid and intact under the system’s trust model. Credentials are optional, so their absence alone does not prove that media is fake.
Provenance records describe recorded history; they are not a truth rating. The C2PA explainer states: “Provenance information alone cannot tell you whether the digital content is true, accurate or factual.” Read the C2PA explainer.
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8. Make a sharing decision that preserves uncertainty
Before sharing, match what you say to what the evidence actually establishes:
- Supported: Share the claim with its relevant date, scope, and context.
- Partly supported: Narrow the wording to the portion the evidence supports.
- Unclear or unverified: Say that it is unverified, or do not pass it on as fact.
AI-generated text should be checked with the same basic evidence-based care as other text. Fluency, confident wording, a detector result, or a provenance record cannot replace checking the underlying claim.
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