AI Detection Explained: Scores, Accuracy and False Positives
Understand what AI detectors can tell you, what they cannot prove, and how to review a flagged draft using sources and writing-process evidence.
By TextUnbot
An AI detector estimates patterns in writing. It does not watch someone compose a document. That distinction matters whenever a score influences a publishing decision, a client conversation or an academic review. Use this guide to choose the right next step instead of treating a label as a verdict.
Quick answer
Quick answer
AI detection is a screening signal, not proof of authorship. Read the tool’s definition of its score, check whether the text fits its supported inputs, and review the writing process before drawing conclusions. A low score does not verify originality; a high score does not establish misconduct.
Start with the question you actually need answered
“Was this written with AI?” is not the same as “Is it accurate?”, “Is it original?” or “Does it follow our policy?” A fact check examines claims. A similarity check looks for matching text. A policy review compares disclosed assistance with the rules. None of those jobs is completed by an AI score alone.
| Your question | Best next step |
|---|---|
| What does this percentage mean? | Read AI detector scores → |
| How reliable is this result? | Evaluate detector accuracy → |
| Why was my own writing flagged? | Handle a false positive → |
| Why do two tools disagree? | Compare conflicting results → |
| Does this mean plagiarism? | Detection vs. plagiarism → |
How do AI detectors work?
Different products use different classifiers and report formats. Broadly, they look for patterns associated with examples of human or machine-generated writing. They do not have a universal signature that identifies every model, every edited draft or every writing style.
Separate the model’s prediction from the interface used to display it. A document classification, a highlighted passage and a percentage of qualifying text are different outputs. Consult the product documentation before comparing them.
A practical review workflow
For example, a polished introduction may look different from a rough draft because an editor revised it. That is a reason to ask about the revision process, not a reason to assume a particular tool was used. This is an illustrative review scenario, not an experiment.
- Save the original draft and the complete report, including the date, tool and settings.
- Check language, length and document-format requirements before interpreting the output.
- Identify the actual concern: undisclosed assistance, unsupported claims, copied material or weak writing.
- Review drafts, notes, source records and version history with the author’s explanation.
- Document what the available evidence supports and what remains uncertain.
Where TextUnbot fits—and where it does not
TextUnbot offers detection and rewriting workflows. Detection can prompt a closer look; rewriting can help you edit tone and clarity. Neither workflow certifies who wrote a document or guarantees acceptance by another detector. If a genuine draft is disputed, preserve it before editing.
When editing is permitted, improve the argument, verify citations and preserve meaning. Do not substitute a target score for a useful, accurate final text.
Read next: Improve wording without losing meaning
Read next: Compare the GPTZero workflow
Common questions
Frequently asked questions
Can an AI detector prove that ChatGPT wrote something?
No. A detector result is a prediction about text, not a record of who wrote it or which application was used.
Should I run every draft through several detectors?
Only if you have a clear review purpose. Repeated tests can produce conflicting labels without resolving the underlying authorship question.
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