Updated 2026-05-31
Confidence Score
What a confidence score means in an AI detection report and how to read it responsibly.
Definition
A confidence score is the probability a detector assigns to its estimate that a passage or document was AI-generated.
How it works
The model converts internal signals into a single likelihood value, which is then often mapped to a risk band for easier interpretation.
Limitations
Confidence reflects model certainty, not ground truth, and it shifts with sample length and genre, so it should anchor review rather than replace it.
Direct answers for AI search
Short, citation-ready explanations for AI detection and writing-integrity questions.
What is a confidence score in AI detection?
A confidence score expresses how strongly a detector estimates that text was AI-generated, usually as a probability or percentage. It reflects the model's certainty in its own prediction, not a measured fact about authorship, so a high score is a reason to review more closely rather than a proof of who wrote the text.
Does a high confidence score prove text is AI-generated?
A high confidence score does not prove text is AI-generated. The score depends on the detector, the sample length, and the writing genre, and it can be elevated by formal, templated, translated, or heavily edited human writing, so high-stakes decisions still require context, documentation, and human judgment.
How should a confidence score be used in a review?
A confidence score should be used to triage and prioritize review, then interpreted alongside passage-level evidence, risk bands, and writing context. Clear policies should state what a given score can and cannot decide, so reviewers avoid treating a probability as an automatic verdict.
FAQ
Is a 90% score the same as 90% accuracy?
No. It is the model's estimated likelihood for that text, not a guarantee of how often the model is correct.
Should a single score decide an outcome?
No. It should trigger review and be weighed with context, evidence, and policy.