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You usually cannot prove that a website article was written by AI by looking at its prose or running it through a detector. The most reliable approach is to check for a disclosure, identify who is accountable for the page, verify its claims against original sources, and treat any detector result as a limited screening signal—not a verdict.
Start with evidence, not writing style
Formulaic phrasing, repetitive structure, a polished tone, or unusual wording may make you curious about how an article was produced. None of those traits establishes authorship: people can write that way, AI-generated text can be edited, and human and AI contributions can be mixed.
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Use style as a reason to check the article more closely, not as grounds to label it. There is no reliable visual shortcut that identifies an AI-written web page.
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Look at the article itself, the author’s page, the publisher’s About page, and its editorial policy. A publisher may explain whether it uses automation, what tasks it uses it for, and how people review the result. Google says that sharing how content was created can give readers context: Google Search Central’s guidance on generative AI content.
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A disclosure can clarify the process, but it does not by itself prove that every claim is sound. Conversely, not finding a disclosure does not show that AI was used; the publisher may not disclose its process.
Find out who is accountable for the page
Check whether the article names an author and whether the publisher provides enough information to identify and contact the organization responsible for it. Relevant expertise can matter for specialized subjects, but a byline alone cannot show whether a person, an AI system, or both produced the text.
Accountability is useful for a different reason: it gives you somewhere to check the author’s qualifications, raise a correction, or ask how a consequential article was prepared.
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Assess the article’s reliability independently of its presumed authorship. Follow links to original documents, data, research, or statements; check quotations and figures against those sources; and make sure the cited material actually supports the claim attached to it. A long reference list is not enough if its links are irrelevant or do not substantiate the text.
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- For a number, find the original source and confirm what it measured, when, and under what conditions.
- For a quotation, compare the wording with the original statement and read enough surrounding context to avoid a misleading excerpt.
- For a high-stakes claim, look for confirmation from authoritative or independent sources rather than relying on a single page.
These checks help whether an article was written by a person, generated with AI, or produced through a combination of the two. Google recommends manual review of AI-generated content for accuracy and trustworthiness in its content guidance.
Understand what AI text detectors can and cannot tell you
Text detectors estimate whether a sample resembles patterns associated with generated text. Their output is a screening result, not proof of who wrote a particular article. A percentage should not automatically be read as the probability that the page was AI-written; that interpretation would require the tool’s method and calibration to apply to the specific text and population being assessed.
Performance depends on the material and evaluation conditions. NIST’s 2024 report says short text can produce results little better than chance in several studies it reviewed. It summarizes findings of about 90% AUC for longer samples of roughly 750 words and about 80% or above for the best detectors after paraphrasing attacks in the reviewed studies. Those are conditional research results—not guarantees for a current detector, a particular website article, or every kind of text. AUC is a measure of ranking performance across thresholds, not the chance that a specific article was AI-written. Read the NIST report.
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Before relying on a tool, check whether its published evaluation matches the text you have. Relevant questions include:
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- What sample lengths and languages were tested?
- Which writing populations and generative models were represented?
- Was mixed or substantially edited writing included?
- What false-positive rate applies at the score threshold being used?
- Was performance independently evaluated on material similar to this article?
NIST notes that detectors have often been developed for English, and that English text written by non-native speakers has been classified as AI-generated more often. It also describes how thresholds trade off false positives and false negatives. A false positive can unfairly damage a writer’s reputation, so do not use a detector alone to accuse someone, impose a penalty, or publicly label their work.
A 2023 study of 12 publicly available tools and two commercial systems found that the systems it tested were not accurate or reliable under its test conditions, and that obfuscation further reduced performance. That study is evidence of limitations in the tools and material it examined at the time, not a current ranking of products. Read the study abstract.
Do not ask a chatbot to identify its own writing
ChatGPT cannot reliably determine whether it produced a piece of text. OpenAI says it has no knowledge of what content could be AI-generated or what it generated, and that replies to questions such as “did you write this?” or “could this have been written by AI?” have no factual basis. A chatbot’s answer is not authorship evidence. OpenAI Help Center: Can I ask ChatGPT if it wrote something?
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Some digital media may carry provenance information, such as Content Credentials, describing how supported content was created or modified. Such records are useful only when the format and services involved support them and the information is available to you. NIST notes that provenance records and detector outputs may be machine-readable without being displayed to readers. No visible record does not establish that content is human-made; a record may never have been created or surfaced.
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Google’s 2026 announcement describes C2PA Content Credentials verification and a staged expansion across Google products. It does not establish that every website article has verifiable text provenance. Read Google’s announcement.
Ask the publisher when the answer matters
If authorship or process has real consequences, contact the publisher rather than turning an uncertain detector score into a public accusation. Ask how the article was researched and edited, whether automation was used, and whether the publisher can explain its review process or share relevant records. Give the author or publisher a chance to respond before making a claim about how the article was produced.
What Google’s AI guidance does—and does not—mean
Google’s guidance does not say that a page should be penalized simply because AI was involved. It emphasizes useful, accurate, original content and warns against scaled content production intended to manipulate Search. Google also says AI use does not give content a special ranking boost. The relevant question for readers is whether a page is trustworthy and helpful, not whether AI was used in isolation. See Google’s Search Central post on AI-generated content and its current guidance.
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