Use an AI detector as one limited signal. Check its scope and sample requirements, protect private material, compare the output with independent evidence, and never turn an unverified score into a verdict about a person.
What an AI detector score can—and can’t—tell you
Most text detectors estimate whether a sample resembles patterns their models associate with AI-generated writing. The number is an output from that provider’s system, not a direct record of who typed the words. Different providers can define scores and thresholds differently, so two percentages should not be treated as a shared measurement scale.
Detectors can make both kinds of mistake: flagging human writing or missing AI-assisted writing. A peer-reviewed 2023 evaluation of selected tools found false positives and false negatives on its test set, and reported that rewriting and translation changed results. That study is evidence for caution, not a current performance ranking: tools, models, and test conditions change.
Fair use: treat a score as a prompt to ask a better question, not as proof of authorship, dishonesty, or intent.
Start with the evidence you need
For writing
Read the provider’s documentation for supported languages, minimum sample length, and what the score means. Very short excerpts, heavily edited drafts, translations, and mixed human–AI writing may be handled differently across tools. Save the exact text and the date of each check if you need to compare later.
For images, audio, and video
Choose a tool built for that media type. Some systems inspect visual or audio patterns; others look for watermarks, file metadata, or signed provenance records. These checks answer different questions, so a text detector cannot validate an image and a missing watermark does not settle where a file came from.
For provenance
Content Credentials based on the C2PA standard can record signed information about an asset’s origin and editing history. A valid credential can help check whether those recorded details remain attached to the asset, but C2PA says provenance alone cannot determine whether the content is true or factually accurate. No credential is also not proof that a file is AI-made.
A practical workflow before you rely on a result
- Define the question. Are you checking writing style, image manipulation, a supported watermark, or a file’s documented history? Pick a tool that actually covers that question.
- Check the rules and privacy terms. If the content is confidential, unpublished, or personal, review the provider’s retention and reuse policy before uploading. Use a non-sensitive sample when you are only learning how the interface works.
- Read the method and limitations. Note the supported language or media, minimum input, score explanation, and whether the provider documents known failure cases.
- Record the context. Keep the original file or passage, the tool name, date, and result. Scores may change when providers update their systems.
- Look for independent evidence. Consider drafts, version history, source files, citations, capture context, and a chance for the creator to explain the work. Keep conclusions proportionate to the evidence.
AI detector questions, answered
Can a detector prove that a person used AI?
No. A detector returns a classification or estimate. The result does not independently establish who wrote a passage or how it was produced. Use other evidence and a fair review process.
Why do two detectors disagree?
Providers use different models, training data, thresholds, and score definitions. Compare the explanations and test conditions, not just the headline numbers.
Does a missing Content Credential mean a file is fake?
No. Credentials are a useful provenance signal when present and valid, but they can be absent or separated from a file. The C2PA specification says provenance records do not decide whether a media claim is true.
Should I upload someone else’s work?
Only when you have a valid reason and permission under the applicable rules. Check provider privacy terms, and avoid submitting private or sensitive content to an unfamiliar service.
Sources and scope
This guide is general information, not an accusation or a guarantee of any tool’s performance. We have not independently benchmarked the listed products; check their current provider documentation before use.
- Weber-Wulff et al., “Testing of detection tools for AI-generated text”, International Journal for Educational Integrity (2023). Findings concern the tools and test conditions evaluated in that study.
- C2PA, Content Credentials Explainer. Describes provenance records and their limits.
- C2PA FAQ. Background on signed credentials and asset history.
Compare 56 listed tools for writing, images, audio, video, APIs, and content provenance.
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