Accuracy explained · Interpret carefully
Accuracy and false positives
A detector has no fixed accuracy apart from its data, threshold, and use case. The meaningful questions are how often it misses AI content, how often it flags human work, and what a positive result means at the base rate in your setting.
01 · Define accuracy
One overall percentage compresses different errors, class proportions, and decision costs into a single number. Separate the metrics before comparing results.
Performance changes with the test set, language, content type, generation model, editing process, and decision threshold. A strong result in one experiment does not automatically transfer to classroom essays, news images, platform-transcoded video, or future models.
A false positive labels human content as AI and can cause an unjust accusation. A false negative misses AI content and can make screening ineffective. Reducing one error commonly increases the other, so the threshold should reflect the real cost of each outcome.
Suppose only 10 of 1,000 items are AI-generated. Even with 80% recall and a 5% false-positive rate on human work, the detector produces about 8 true positives and about 50 false positives. In a low-base-rate setting, a flag is especially weak evidence by itself.
02 · Account for the medium
An evaluation number from one medium does not apply to another. Input length, compression, and transcoding change the evidence a detector can actually observe.
Short passages, translations, second-language writing, code, tables, and fixed formats may be naturally repetitive or unusually regular. Human editing can also remove patterns from AI-generated text, so one threshold can have very different errors across genres.
Open the AI text detectorScreenshots, crops, filters, denoising, platform compression, and low light all change texture, color, and edges. Illustrations and templates may be flagged incorrectly, while edited generated images may no longer contain the features a detector originally learned.
Open the AI image detectorPlatforms rewrite bitrate, resolution, container, and duration metadata, while repeated transcodes add audiovisual artifacts. A metadata-only tool cannot reliably attribute those changes to a generation model and must be paired with the original file and frame continuity.
Open the AI video detector03 · Evaluate the claim
Before trusting a headline number, inspect where samples came from, how the threshold was chosen, whether testing was independent, and whether each error was reported.
The test set should include the languages, genres, devices, compression paths, and generation models expected in actual use, plus difficult human examples. Data used during development cannot also serve as the final evaluation without making results look overly optimistic.
Recall shows how much truly AI-generated content was caught, while precision shows how much flagged content actually belonged to the target class. The confusion matrix exposes the underlying counts of true positives, false positives, true negatives, and false negatives.
Different thresholds trade precision for recall. An 80% interface score is a calibrated probability only if calibration data show that about eight in ten similar cases belong to the target class; many rule scores and classifier confidence values do not meet that condition.
04 · Use results safely
Models and content keep changing. A responsible process monitors failures, preserves provenance evidence, and lets people inspect and correct automated results before consequential decisions.
New generation models, languages, platform processing, and evasion methods move live inputs away from an old test set. Monitor errors by medium and population, then re-evaluate after distribution shift instead of treating one launch-time number as permanent.
A score can decide what to inspect next but cannot replace provenance checks. Original files, content credentials, version history, reverse search, and publisher statements reduce the risk that several similar detectors share the same hidden bias.
When education, employment, reputation, or legal consequences are involved, a qualified person should inspect the original material, error costs, and alternative explanations. The person affected must be able to provide evidence and correct the record.