What an AI detector actually reports
Most text detectors classify an input using patterns learned or measured from examples. Depending on the system, those patterns may include word choice, sentence structure, or model-specific statistical signals. The result is a model output for the text provided—not a record of how that text was produced.
Ghostiq’s detector sends a passage for processing and displays an estimated score and returned highlights. This is a result for the passage you submitted, not an independent Ghostiq accuracy benchmark. Different products may use different models and thresholds, so their scores are not directly interchangeable. See the privacy policy for the current processing details.
A detector can flag human writing or miss AI-assisted writing. Do not use a single score as proof of authorship, misconduct, or intent.
Why detector results can be wrong
Detector performance depends on the model, text length, genre, language, and examples used to evaluate it. Editing and mixed human–AI workflows can also change the signal. Independent evaluations have found that results vary across tools and test conditions. In one evaluation, OpenAI reported that its experimental classifier identified 26% of AI-written samples as “likely AI-written” and incorrectly labeled 9% of human-written samples; the results were specific to that classifier and evaluation set, not a universal accuracy rate.
A later practical evaluation found that detector performance could change substantially under different conditions and emphasized reporting both true-positive and false-positive rates. These studies are reasons to treat detector output as uncertain—not to infer that every detector has the same accuracy.
OpenAI’s classifier evaluation · NAACL 2025 practical examination of AI text detectors
Why two AI detectors may disagree
Tools can differ in their training data, scoring methods, decision thresholds, and supported languages. They may also process text of different lengths or highlight different portions. A disagreement does not tell you which system is correct; it shows that the classification is uncertain.
How to review a flagged passage responsibly
- Read the highlighted text in context. A phrase or sentence is not evidence of who wrote it.
- Check the document’s drafts, notes, citations, and revision history when authorship matters.
- Give the writer a chance to explain their process and follow the relevant institution or workplace policy.
- Do not make a high-stakes decision from an automated score alone.
Using writing revision tools
A humanizer can suggest a different phrasing or rhythm, but it can also change meaning. Ghostiq’s AI text humanizer returns an edited draft for you to review; it does not guarantee that another detector will classify the text differently. Check facts, citations, and your own voice before using a rewrite.
Try Ghostiq’s writing tools
Check a passage, review the estimate, or explore a revision. Results are signals for review, not proof of authorship.
Open the AI writing detectorPrivacy before you paste
Ghostiq sends tool submissions for external processing to return results. Providers have their own data practices. Read the privacy policy and avoid submitting confidential material unless you are permitted to share it for processing.
Common questions
Can an AI detector prove who wrote a text? +
No. A detector returns an estimate based on text patterns. It cannot establish authorship or prove misconduct.
Why do AI detector scores differ? +
Different systems use different models, examples, and thresholds. A score from one tool cannot be directly compared with another.
Can a humanizer guarantee a lower detector score? +
No. Detector results vary, and a rewrite can change meaning. Review edits yourself and follow the rules that apply to your work.
Explore Ghostiq
- Free AI writing detector — review an estimate and highlighted passages.
- AI text humanizer — compare a revision with your original draft.