Known Limitations

No AI detector can guarantee perfect authorship classification across all topics, lengths, and languages.

HumanLike provides strong signals and editing controls, but final quality and compliance decisions still require human judgment.

This page lists practical limitations teams should cite when documenting policy.

Model and domain variance

Writing domains with repeated templates can resemble AI style and may increase false positives in detection.

Highly edited human text can also trigger mixed or AI-like scores.

Detection confidence is generally stronger with enough text context. Very short text fragments are less stable.

Multilingual detection behavior depends on available pattern coverage for each language.

Detector output should not be used as the sole basis for punitive action without supporting review evidence.

Humanizer output quality should still be reviewed for factual accuracy and brand voice alignment.

Can I use detector output as sole disciplinary evidence?

No. Best practice is to treat detection as one input in a broader review process.

Do rewriting tools remove all detection risk?

No. Rewriting can reduce repetitive signals, but no system can promise universal invisibility.