AI percentage explained
What an AI detector score can—and cannot—tell you
An AI detector estimates whether a piece of text resembles patterns associated with machine-generated writing. EraseGPT presents that estimate as an AI percentage so you can compare the strength of the signal, but the number is not proof of who wrote the text and it is not a literal measurement of how many words came from an AI system.
In briefUse the percentage as a screening signal that tells you when closer review may be worthwhile. A higher score means the detector found a stronger pattern match according to its current analysis; a lower score means it found a weaker one. Neither result establishes authorship on its own. Context, drafts, citations and a conversation with the writer are better evidence for consequential decisions.
How to check AI percentage in a useful way
1. Submit enough meaningful text
Use a coherent passage rather than a title, list or handful of sentences. Very short samples provide less linguistic context, while copied prompts, references and boilerplate can distort what you actually want to assess.
2. Read the percentage as a probability signal
The result summarizes pattern-based analysis. It does not identify the exact authoring process, reconstruct edit history or prove that a particular model produced the text. Avoid converting it into a claim the result does not support.
3. Compare the result with real-world evidence
Look at earlier drafts, notes, version history, citations, assignment requirements and the writer's explanation. If the result matters, repeat the review with the clean original rather than excerpts changed by formatting or transcription.
Why AI detection can produce false positives
Detectors infer patterns from language; they do not observe the act of writing. Human and generated text overlap in vocabulary, sentence structure and predictability, so classification errors are unavoidable. A polished human passage can look statistically regular, while carefully edited AI output can look less regular.
- Short, highly structured or formulaic writing may not contain enough distinctive context for a stable assessment.
- Technical definitions, standard instructions and common academic phrasing can resemble patterns seen in generated text.
- Translation, grammar correction and accessibility tools can change the linguistic features a detector evaluates.
- Writers using English as an additional language may use consistent constructions that some detectors misclassify.
- Model updates, new writing tools and human editing can change detector performance over time.
How to interpret a low or high AI score
A low AI percentage does not certify that a document is entirely human-written. It only means the submitted version produced a weaker AI-like signal in that analysis. Likewise, a high percentage does not prove misconduct, identify a specific model or tell you which portions were generated. Treat both ends of the scale with the same care.
For your own draft, use an unexpected score as an editing prompt. Re-read generic transitions, unsupported claims and repetitive sentence patterns, but revise them because the writing becomes more specific and accurate—not merely to move a number. Preserve evidence of your process when authorship may later matter.
When reviewing someone else's work, do not confront the writer with a percentage as if it were a finding of fact. Ask open questions about sources and process, make the relevant policy clear, and allow a fair opportunity to provide drafts or explain permitted assistance.
A responsible AI-content review checklist
- Confirm that the submitted text is the intended final version and has not been altered by copy-and-paste errors.
- Separate AI-authorship questions from plagiarism questions; generated text and copied text are different issues.
- Record the score, date and context if the review is part of a documented process.
- Seek corroborating evidence before any academic, workplace or publishing decision.
- Let a human reviewer make the final judgment and provide a route to challenge mistakes.
EraseGPT does not claim that one score can settle authorship. Its public methodology does not provide the independent, model-by-model validation needed to make a verified accuracy claim. The most defensible use is narrow: identify a possible signal, investigate it carefully and communicate the uncertainty.