AI Detection Methodology

HumanLike detection produces an AI-likelihood score and sentence-level labels such as human, mixed, and AI.

Scores are directional signals, not legal proof of authorship, and should be interpreted with context.

This page explains how to use detector outputs for review workflows and policy decisions.

Scoring model behavior

The detector evaluates text patterns associated with machine-generated prose and combines them into a bounded score.

Higher values indicate stronger AI-like patterns, while lower values indicate more human-like language variation.

Sentence labels help reviewers find localized high-risk passages instead of relying on one document-wide score.

Mixed labels indicate uncertainty and should trigger human review rather than automatic rejection.

Use score bands as triage signals in editorial or academic workflows, then review highlighted passages manually.

Detector output should be combined with metadata, source history, and policy checks for final decisions.

Is a high score definitive proof of AI authorship?

No. It is a probabilistic signal and should always be validated with human review.

Can short text produce unstable results?

Yes. Very short samples reduce statistical confidence, so longer samples usually yield better signals.