What happens when you paste text
Feature extraction
The tool measures wording, rhythm, structure, and repetition patterns.
Model comparison
Those signals are compared to what the detector has seen in human and AI corpora.
Score assembly
Weighted signals are compressed into a probability-like output.
Threshold decision
Each product applies its own cutoff for labels like "likely AI."
Change the model, feature weights, training corpus, or threshold and you change the result.
The three detector engines
Statistical detection
Uses metrics like perplexity and burstiness to measure predictability and variation.
False-positive risk: formal or heavily edited human prose can look statistically "machine-like."
Classifier detection
Binary models trained on labeled human/AI examples output a category likelihood.
False-positive risk: performance drops when new models or new genres fall outside training coverage.
Pattern matching
Looks for concrete structures overrepresented in AI drafts: templates, scaffolding, compressed rhythm.
False-positive risk: catches overlap patterns, not authorship intent.
WROITER uses pattern matching for transparency: you can inspect each triggered flag instead of trusting a black box. Details: How It Works.
Watermarking is a different lane
Watermarking is not general detection. It only works when generation and verification both support the same watermark scheme.
- Good for: provider-controlled verification pipelines.
- Not good for: mixed-source text, edited drafts, paraphrased text, and cross-tool review workflows.
Why two detectors disagree on the same text
Different training corpora
Model A may be tuned to one generation style while Model B saw a different one.
Different feature weights
Rhythm-heavy systems and phrase-heavy systems diverge on mixed-signal passages.
Different thresholds
One product labels at 50, another at 70. Same raw signal, different verdict.
Different update clocks
Detectors lag behind new model releases, so drift is expected.
Disagreement is useful information: it usually means ambiguity, not certainty.
Why false positives persist
Detectors key off surface patterns that can come from perfectly human writing:
- Academic and policy writing: constrained structure and conservative phrasing.
- Heavy editorial polish: reduced stylistic variation after revisions.
- Second-language writing: safer lexical choices that overlap with model defaults.
- Institutional/legal genres: formal repetition by design.
See the False Positive Hall of Fame and Limitations for concrete failure contexts.
How to use detector output responsibly
Reliability implications: Do AI Detectors Work?.