People deserve a clear route to correct an AI-review result that lacks context. An escalation path makes that possible without forcing staff to improvise under pressure. It also protects the organization: decisions become easier to explain, compare, and improve.
Product reference: the public Contact page captured on July 19, 2026.
Last reviewed: July 19, 2026 Use case: a reviewer or affected person challenges an AI-detection outcome
The path in five steps
- Notice: tell the person what was reviewed, what the score means, and that it is not conclusive proof.
- Context window: allow drafts, sources, version history, or a brief explanation within a stated timeframe.
- Independent review: assign someone who did not make the initial decision for material consequences.
- Written outcome: state the evidence considered, policy applied, and any corrective action.
- Process learning: log the outcome category so recurring errors can change the workflow.
Worked example: the escalation should change the evidence, not repeat the score
Case sample: a freelance writer disputes a high review signal and provides dated outline notes, client interview recordings, and a version history showing incremental revisions.
The appeal reviewer should evaluate those materials against the editorial policy. Running the same text through another detector may be supplementary, but it should not replace the new evidence. If the decision changes, record why; that reason may reveal a weakness in the original threshold or training guidance.
Define outcomes in advance
| Outcome | Meaning | Follow-up |
|---|---|---|
| Closed—insufficient concern | Evidence does not support further action | Remove the review flag where policy allows |
| Coaching | Work needs clearer sources or disclosure | Offer revision without an accusation |
| Formal review | Independent evidence raises a policy issue | Use the organization's documented process |
Avoid outcome labels that imply guilt before a full review. A neutral vocabulary helps reviewers stay focused on evidence.