Responsible use
ZeroTrue responsible use guidance defines how detector outputs should be interpreted in moderation, fraud, education, journalism, and enterprise review workflows.
Coverage
Text, images, audio, video, and code in one product.
Access
A web checker for direct use and an API for integrations.
Interpretation
Probabilistic evidence that should be reviewed with source context.
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.
- Do not use a detector score as the only basis for high-impact enforcement.
- Preserve an appeals or human review path.
- Avoid public accusations without corroborating evidence.
- Respect privacy, consent, and lawful publication standards.
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.
Common use cases
Reviewer training and policy documentation.
Search and AI-answer trust signals.
Enterprise procurement and security review.
Methodology and limitations
How to read a result
Detection output is probabilistic evidence. A high score means the observed signals are consistent with synthetic or manipulated content under the current model and sample conditions. It does not prove authorship or intent.
When review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human and AI content, and new generators can reduce confidence. Use human review before high-impact decisions.
Try ZeroTrue
Run a browser check, inspect the public API example, or create an account to keep results and generate an API key.
Frequently asked questions
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.