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There is no dependable single tell that proves content was made by AI—or by a person. Start with the file’s provenance and creation history, then look for supported technical signals and corroborate them with independent evidence. A detector score or a familiar writing style is not proof of authorship.
What can—and cannot—establish that content is AI-generated?
“AI-generated” and “human-made” are not always opposites. Someone may use AI to draft, translate, edit, or produce only part of a work. A watermark or other provenance signal may indicate that a supported system generated or processed some content, but it does not reveal how much a person contributed.
Keep these questions separate: provenance asks where content came from; authorship asks who created it; accuracy asks whether its claims are true; and ownership or legal responsibility are separate matters again. A technical signal does not settle all of them.
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OpenAI says its provenance tools look for supported signals associated with OpenAI, not every AI system. A positive result is evidence of a supported signal—not proof of accuracy, ownership, legal responsibility, or context. A negative result is not proof of human authorship: the content may be from an unsupported system, predate a rollout, use an unsupported format, or have lost metadata or a degraded watermark through editing. See OpenAI’s guidance on provenance signals.
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How to check an image or audio file
- Work from the original file. Preserve the exported original and its context. Cropping or converting an image can interfere with a check; OpenAI recommends checking without those changes.
- Use a checker that supports the file and signal. A tool can find only the provenance signals it is designed to recognize. OpenAI’s audio guidance says clips between 10 and 60 seconds generally produce its best results.
- Interpret the result narrowly. Say that a supported signal was detected, rather than treating it as proof the file is accurate, untouched, or wholly AI-made.
- Trace the source independently. Look for the earliest available publication, relevant records, and corroborating reporting. A missing signal does not establish that a person made the file.
How to assess text and AI detector results
Text checks can look for provider-specific watermarks or estimate whether prose resembles AI output. Those are different kinds of evidence, and neither settles who wrote the text. Before relying on a detector, check which provider, model, region, date range, passage length, and editing conditions it supports—and whether you can access an authorized tool at all.
As of OpenAI’s October 5, 2026 announcement, access to its text detector initially requires approval for researchers and expert organizations. The announcement describes a rollout for eligible ChatGPT and Codex output in the EU and opt-in API watermarking for select models. Availability can change, and these details should not be assumed to cover every model, user, or region. OpenAI characterizes the technology as early and significantly limited in its approach to EU text provenance rules.
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What OpenAI’s reported evaluation shows
OpenAI reported results for its evaluated text watermarking system on psychology passages. At a target false-positive rate of 1%, it detected about 80% of 200-token passages and about 95% of 400-token passages. In 400-token passages, detection was about 92% before synonym replacement, 66% after 10% of words were replaced with synonyms, and 17% after 25% replacement. These are OpenAI’s results under its stated evaluation conditions—not universal performance rates for AI detectors.
The figures illustrate why passage length and editing matter. A missed signal does not establish human authorship, and results from one system cannot be generalized to other detectors. OpenAI also says its watermark does not measure how much human judgment, editing, or creativity went into a passage.
How to combine evidence without overclaiming
- Check provenance first: preserve the original and trace the earliest available source or version history.
- Check what a technical result covers: identify the supported provider, content type, format, and signal, and whether the file or passage was edited.
- Verify the content separately: compare factual claims with independent records or reliable reporting. A provenance signal does not verify truth.
- Describe the finding precisely: “A supported signal was detected” or “No supported signal was found” is more accurate than declaring content AI-generated or human-made based on one check.
- For consequential allegations, seek corroboration: treat a detector result as a lead, not proof, and give the creator a chance to explain the source and editing history. This is prudent editorial practice, not a legal standard.
Why no single detection method works for every case
A 2026 European Commission technical report groups text-provenance approaches into watermarking, structural marking, metadata, logging, and AI-generated-text detection. It assesses approaches across effectiveness, robustness, reliability, accessibility, and interoperability. They answer different questions: some attach a signal, some preserve records or metadata, and others infer from text patterns. Their usefulness depends on the method, content, transformations, system, and access to verification. The report does not establish one category as uniformly superior. See the European Commission report on technical solutions for marking and detecting AI-generated text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When disclosure rules may apply
Disclosure requirements depend on jurisdiction and use; they are not a universal rule that every AI-assisted paragraph must be labeled. The European Commission says Article 50 of the EU AI Act applies from August 2, 2026, with specified marking and disclosure obligations. Its examples include deepfakes and certain public-interest text published without human review or editorial control. Consult the Commission’s guidelines on AI transparency obligations for the applicable scope; do not infer that every instance of AI-assisted writing falls under the same requirement.
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