Five human-review patterns

The AI prepares a complete action, but execution waits for a person. Examples include a drafted public post, a proposed refund, or a database update.

The reviewer needs to see:

• the proposed action in its final form;
• the evidence or source records used;
• the important policy limits;
• what will happen after approval;
• a clear way to reject or request changes.

Approval works well when actions are consequential but not so frequent that reviewers become overloaded. It fails when the button becomes a ritual. If people approve almost everything in seconds, measure whether they have enough time and information to detect errors.

Sometimes the AI output is useful but rarely ready as-is. The human’s job is to revise it, not merely accept or reject it. This pattern fits customer replies, reports, translations, and creative work.

Track how much editing is required. Small, predictable edits suggest the prompt, source data, or template can improve. Large edits may mean the system is not saving work. An edit interface should preserve the original draft and the final version so the team can learn what changed.

Do not hide uncertainty behind polished text. Show citations, missing fields, and warnings beside the draft. The reviewer should not have to reverse-engineer why the AI wrote something.

The system handles routine cases and sends uncertain, high-risk, or out-of-policy cases to a person. This is often called human-on-the-loop when people supervise exceptions rather than approve every item.

Useful escalation triggers include:

• required information is missing;
• sources disagree;
• the requested amount exceeds a threshold;
• the input resembles a known abuse pattern;
• the model cannot produce valid structured output;
• a policy rule says a person must decide;
• the situation is unlike tested examples.

Escalation needs a safe default. If no reviewer responds, should the action pause, expire, or proceed? For high-impact decisions, “proceed after timeout” may silently remove the safety gate during busy periods.

Reviewing every low-risk action may cost more than it protects. Instead, people can review a sample, a daily batch, or items selected by risk signals.

Random samples estimate general quality. Risk-based samples focus attention on likely failures. Use both: risk-only review can miss a new failure that the detector does not know how to flag.

Batch review fits reversible, observable actions such as tagging support tickets or classifying internal documents. It is weaker for actions where one mistake can cause serious harm. A daily review cannot prevent a private document from being disclosed at noon.

Sampling is a monitoring pattern, not permission to ignore individual harm. Define what happens when the sample finds an error: expand the sample, pause automation, correct affected records, and investigate the cause.

Some actions require two independent people or roles. One person prepares or approves, and another confirms. Financial transfers, security changes, and release of highly sensitive information often use this pattern even without AI.

AI does not replace either side of dual control. It can prepare evidence, check completeness, or highlight discrepancies. The independence matters: two approvals from the same person, or a second reviewer who simply trusts the first, do not provide the intended protection.

Use dual control selectively because it is expensive. Reserve it for actions where insider risk, fraud, or catastrophic mistakes justify the additional delay.

Use this quick guide:

• Choose approve when outputs are usually ready but actions are consequential.
• Choose edit when human expertise improves the content on most cases.
• Choose escalate when routine and exceptional cases can be separated reliably.
• Choose batch review when actions are low risk, reversible, and measurable.
• Choose dual control when one person should not have unilateral authority.

Patterns can be combined. A support assistant may draft replies for editing, automatically send low-risk status updates, escalate threats or refunds, and sample sent messages for quality. Describe each branch explicitly rather than attaching one “human review” label to the whole system.

For any pattern, write one sentence: “The reviewer decides whether ___ based on ___.” If that sentence is unclear, the interface and training will also be unclear.

Then define the available actions. “Approve” and “reject” may not be enough. Reviewers may need “edit,” “request more information,” “escalate to specialist,” or “mark as system error.” Every option should have a known next step.