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A transparent explanation of AI detector uncertainty, human and AI writing overlap, edited text complexity and false positive or false negative risk.
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 detectors estimate patterns. They cannot see intent, authorship history, private drafting process or every editing step behind a piece of writing.
Formal human writing can share signals with AI-assisted writing. This is common in academic abstracts, resumes, business templates, policy language and highly edited copy.
AI-assisted writing may be rewritten, paraphrased, shortened, translated or merged with human revisions. Humanised AI text can reduce visible signals while still leaving mixed patterns.
A false positive means human writing is flagged as AI-like. A false negative means AI-assisted writing is not flagged strongly. Both outcomes are possible, so decisions should not rely on the detector alone.
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.
Academic writing often uses structured phrasing, formal transitions, abstract language and consistent tone, which can overlap with AI-like writing signals.
No. Rewriting, editing and mixed authorship can reduce visible signals. Results should be used as guidance, not certainty.
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.