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A clear explanation of why AI detection results should be used carefully and reviewed with context.
Core principle
AI detection results are guidance only.
No detector can prove authorship with certainty. These pages explain how to interpret results without overstating what automated analysis can prove.
Trust framework
The detector reviews writing behaviour such as repetition, structure, phrasing consistency, sentence variety and context-specific signals. It explains what influenced a result instead of presenting a black-box accusation.
Human and AI writing can overlap, especially in academic, formal, translated, edited or templated text. False positives and false negatives are possible, so results should guide review rather than decide outcomes alone.
Reports are designed around metadata, scores, signals, timestamps and guidance. Raw submitted text, uploaded file contents, prompts and provider payloads are not stored in reports.
Use detector outputs to support human review, editorial improvement, policy conversations and proportionate follow-up. Avoid punishment-only decisions based on an automated result.
Methodology detail
AI writing detectors estimate likelihood from patterns. A high AI probability can suggest review is needed, but it does not establish intent, authorship or misconduct.
Formal, concise, translated, edited or templated human writing can sometimes look AI-like. Reviewers should consider the author, context, writing purpose and available supporting information.
AI-assisted writing can be edited, combined with human writing or heavily rewritten. A low AI probability does not guarantee that no AI assistance was used.
The safest workflow is to use detection as one input alongside human review, policy context, communication with the author and proportionate next steps.
Methodology pages explain the review approach, known limitations and why results should not be treated as proof.
Available resourceReports organise confidence, writing signals, recommendations and limitation notes into a review-friendly format.
Available resourceThe public status and transparency pages explain service availability, research principles and responsible claims.
Available resourcePrivacy pages explain temporary processing, report ownership and the platform's data-minimisation approach.
Available resourceFAQ
Short answers for responsible interpretation and privacy-first use.
No detector score should be used alone for high-impact decisions. Human review and appropriate process are essential.
AI writing and human editing overlap. Honest accuracy language protects users from over-trusting automated scores.
Content discovery
Move from methodology into practical guides, privacy pages and product workflows.
Transparency
Explore the transparency pages that explain methodology, privacy, limitations and responsible use.
AI detection results are guidance only. No detector can prove authorship with certainty, and important decisions should include human review and appropriate context.
Scan reports are designed to show scores, signals and guidance without storing the original submitted text.
Read privacy detailsInternal research and benchmark work is used to improve calibration and understand failure modes. Public pages avoid unverifiable accuracy claims and do not expose private datasets or raw benchmark text.