False positives are not an edge case to hide in a footnote. They are a predictable risk whenever a system infers how text may have been produced from patterns in the text itself. The remedy is not to abandon review; it is to design a process that lets a person challenge, explain, and correct an automated signal.
Product reference: the public AI Text Detector page captured on July 19, 2026.
Last reviewed: July 19, 2026 Use case: a student, writer, or employee disputes a detection result
First rule: pause the consequence, preserve the evidence
If a score triggers concern, preserve the original text and record the tool, date, settings, and excerpt reviewed. Do not overwrite the file, publish an accusation, or ask the person to “prove innocence” before explaining the process. A review signal should create a question, not an irreversible outcome.
A three-part response
- Explain what happened. Say that a tool identified patterns worth reviewing and that the result is not proof of authorship.
- Invite contextual evidence. Accept drafts, version history, sources, notes, a short oral explanation, or a demonstration of the writing process according to the relevant policy.
- Use an independent reviewer for high stakes. A second reviewer reduces the risk that the person who raised the concern also becomes the sole judge of it.
Worked example: formal prose can look unusual without being inauthentic
Demonstration sample: “The committee's conclusion follows from the interaction between limited evidence, institutional incentives, and uncertainty about long-term effects.”
The sentence is abstract and balanced. It may deserve an editor's request for a concrete example, but it does not demonstrate AI use. A useful question is: “Which evidence and incentives are you referring to?” A poor question is: “Which model wrote this?”
Evidence ladder
| Level | Example | Appropriate action |
|---|---|---|
| Low | One probability score | Offer feedback or a routine source check |
| Moderate | Score plus unsupported citations | Ask for sources and drafting context |
| High | Verified copied text or fabricated references | Escalate under the written policy |
Keep detection output in the first two levels unless independent evidence changes the situation. Re-scoring the same text with several tools can create a misleading pile of numbers, not independent confirmation.
Write the resolution down
Record the concern, evidence considered, reviewer, outcome, and whether a correction was made. If the concern was not substantiated, record that too and avoid retaining a stigmatizing label. This makes appeals possible and gives the organization a way to measure whether its thresholds are harming particular groups or document types.
NIST's risk-management guidance frames generative-AI controls as context-dependent. In practice, that means a detector threshold appropriate for a voluntary editorial check may be inappropriate for a disciplinary or employment decision.