Interpretation guide

How to Interpret AI Detector Scores

Short answer: an AI detector score is a directional output from a particular model, version and threshold applied to a particular file. Unless a provider publishes calibrated evidence to the contrary, “80” should not be read as an 80% probability that AI made the image, and it says nothing by itself about authorship or intent.

Read four fields together

FieldQuestion it answersWhy it matters
Signal strengthHow strongly did this model respond?The scale belongs to that detector; it is not automatically a probability.
Threshold stateDid the score cross the configured decision boundary?A nearby score can change labels after calibration or a version update.
Model versionWhich detector produced the output?Outputs from different versions are not safely interchangeable.
Evidence scopeWhich image, page, frame or clip was checked?A selected unit does not justify a verdict about the complete document or recording.

Why two tools can disagree

Detectors can use different training sources, generator coverage, transformations, preprocessing and thresholds. A screenshot may reach one service at a different size or compression level than another. Disagreement is therefore evidence about model uncertainty, not a reason to average the scores into a new percentage.

What Made It? keeps available model channels separate and requires stronger independent agreement for a strong Beta result. A single channel is presented as a lower-confidence review indicator. The current release status is documented on the Research page.

Decision rules for real-world use