Research transparency
ZeroTrue publishes research pages that document benchmarks, datasets, test methodology, limitations, and failure cases.
Report example
Research transparency checklist
- Dataset source and collection assumptions.
- Model and detector version tracking.
- Failure examples and ambiguous samples.
- Metrics that separate accuracy, precision, recall, false positive rate, and false negative rate.
Search intent
E-E-A-T and trust intent for Research transparency
Primary evidence
Dataset source and collection assumptions., Model and detector version tracking., Failure examples and ambiguous samples.
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
What this page documents
AI detector websites need explicit trust infrastructure because automated authenticity claims can affect people, publishers, businesses, and security teams. This page turns that trust model into a clear operating policy.
- Dataset source and collection assumptions.
- Model and detector version tracking.
- Failure examples and ambiguous samples.
- Metrics that separate accuracy, precision, recall, false positive rate, and false negative rate.
Evidence standard
ZeroTrue treats every result as a probabilistic evidence packet. The platform should expose the score, model version, input modality, supporting indicators, and known conditions that can weaken confidence.
- False positives and false negatives are tracked separately.
- Confidence bands are preferred over binary verdict language.
- High-impact decisions require a human review path.
- Data handling and retention must be clear before scan submission.
Publication standard
Research, reports, datasets, and benchmark pages should identify what was measured, what was excluded, and how results can fail. That transparency is part of the product, not an afterthought.
- List dataset source, size, modality, and collection assumptions.
- Publish limitations next to performance claims.
- Separate marketing claims from measured findings.
- Keep historical benchmark pages stable for citation.
Use cases
Reviewer training and policy documentation.
Search and AI-answer trust signals.
Enterprise procurement and security review.
Sample report preview
Media preview
Safe sample, redacted upload, or generated demonstration asset.
Public reports should only expose media that is lawful, consented, and safe to publish.
Confidence
Trust signal: documented
Dataset source and collection assumptions.
Evidence item linked to score calibration, source context, and known uncertainty.
Model and detector version tracking.
Evidence item linked to score calibration, source context, and known uncertainty.
Failure examples and ambiguous samples.
Evidence item linked to score calibration, source context, and known uncertainty.
Metrics that separate accuracy, precision, recall, false positive rate, and false negative rate.
Evidence item linked to score calibration, source context, and known uncertainty.
Evaluation table
| Criterion | What to check | Why it matters |
|---|---|---|
| Coverage | Text, image, audio, video, code. | Synthetic media risk rarely stays in one format. |
| Explainability | Score, indicators, timestamps, metadata, limitations. | Reviewers need evidence, not a black-box verdict. |
| Accuracy risk | False positives, false negatives, calibration. | High-impact workflows require documented uncertainty. |
| Workflow fit | API, batch, reports, retention, reviewer queues. | Search traffic must convert into a usable product path. |
Methodology and limitations
How to read the score
Detection output should be read as calibrated evidence. A high score means the observed signals are consistent with synthetic or manipulated media under the current model and sample conditions. It does not prove authorship, intent, or model attribution by itself.
Where review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human-AI content, and unseen generators can reduce confidence. Use human review, source context, and policy thresholds before high-impact enforcement.
Next step
Match the action to the visitor intent: detector pages should lead to a scan, research pages to a downloadable report, enterprise pages to a demo, and developer pages to API keys or playground examples.
FAQ
Why does an AI detector need a methodology page?
Because detection is probabilistic. Users need to understand score calibration, limitations, false positives, false negatives, and when human review is required.
Should detector output be used for punishment automatically?
No. ZeroTrue positions detector output as evidence for review workflows, not as an automatic enforcement decision for high-impact cases.
What should be included in public research?
A useful research page should include dataset assumptions, test conditions, metrics, failures, limitations, and a reproducible explanation of the workflow.
Can this help Google and AI search trust the site?
Yes. Transparent methodology, structured data, stable citations, and limitation language all support authority signals better than generic claims.