How to compare AI detector APIs
Do not compare AI detectors by copying vendor accuracy claims into a spreadsheet. Compare the decision workflow: evidence, reproducibility, false-positive behavior, data residency, and how uncertainty is handled.
Short answer
When evaluating Humaniser, Originality.ai, GPTZero, Sapling, or any other detector, use the same labelled sample, the same threshold policy, and the same review process. Treat vendor claims as context until they have been reproduced on your corpus.
Comparison criteria
| Criterion | Why it matters |
|---|---|
| Reproducible evaluation | Your content domain may behave differently from public benchmark text. |
| False-positive reporting | Formal, ESL, citation-heavy, and templated writing can be harder to classify safely. |
| Abstention | A detector that can say manual review avoids forcing uncertain text into a binary label. |
| Per-sentence rationale | Reviewers need to see the spans behind the verdict, not only a percentage. |
| EU or self-hosted deployment | Data residency and operational control can be decisive for regulated workflows. |
| Analysis records and versioning | Audit records need model references, timestamps, and stable evidence. |
Minimal evaluation protocol
1. Freeze the candidate detector version and settings.
2. Build a labelled sample from your actual workflow.
3. Separate human, AI-assisted, fully generated, ESL, and template-heavy text.
4. Measure false positives and false negatives at the threshold you would enforce.
5. Review abstentions separately instead of treating them as errors.
6. Keep the minimum analysis metadata, model versions, and reviewer notes with the run.How Humaniser wants to be compared
Humaniser should be evaluated on the same corpus and policy threshold as competing detectors. The useful comparison is not "who claims the highest accuracy"; it is whether the workflow can reproduce the result, inspect sentence-level rationale, keep text inside the required jurisdiction, and preserve a reviewer-friendly audit trail.
What not to claim
- Do not claim that one detector is better than another without a published test set and metric.
- Do not treat a detector score as proof of authorship.
- Do not average away language or content-type failures.
- Do not enforce a threshold before reviewing false positives on your own corpus.
Humaniser comparison posture
The strongest Humaniser posture is operational, not rhetorical: reproducible evaluation, EU-resident or private deployment options, abstention, per-sentence evidence, and unsigned scan details. Those claims can be inspected without asserting unverified superiority over a named vendor.