Human-in-the-Loop AI Governance Checklist

Human-in-the-loop is only a control if ownership, review quality, escalation, and authorization records are defined. A person “in the process” without authority, qualifications, or a retained decision is theater.

Use this checklist before AI-assisted outputs influence operational action. It is a planning aid — not a certification, audit, or legal determination.

Teams often declare a workflow “human-in-the-loop” because a reviewer can see the output. That does not answer who may reject it, who may override it, what evidence the reviewer must consider, or what is retained after authorization.

Consequential use needs those answers before go-live. The human-in-the-loop AI product page describes how Smart Logic AI treats authorization as an operational control; this page is the planning instrument for whether your own design is complete enough to operationalize. For agency-facing programs, pair the checklist with the federal AI governance implementation guide and retain outcomes using Decision Ledger field guidance.

The interactive checklist below covers decision ownership, reviewer qualifications, risk classification, approvals, evidence, escalation, overrides, provenance, monitoring, and periodic control review. Work through one use case at a time. A control that exists for drafting may be missing for external action.

This checklist is a planning aid and does not constitute legal, regulatory, audit, or certification advice.

Foundational controls documented. Many required roles and records appear defined. Still validate against written policy and confirm the runtime workflow can produce the same evidence.

Significant governance gaps remain. Continue design work before operational reliance. Missing owner, reviewer, or authorization records are blocking issues for high-impact uses.

Formal review required before operational use. Ownership, review, or authorization is incomplete. Do not treat model output as an institutional decision in that state.

Reviewer and authorizer are the same overworked operator, with no escalation path. Materiality thresholds are undefined, so every output is “reviewed” equally — which usually means none are reviewed well. Model and version are not retained, so the reviewer cannot later explain what they approved. Exceptions happen in chat. Monitoring after authorization is absent, so a bad pattern is discovered only in an incident.

Close those gaps in design, then connect the use case to a workflow that can preserve reviewer actions in a Decision Ledger. Anchor the program with an AI governance framework versus platform distinction and, where inventory governance is already mature, decision governance for authorization evidence. Pair planning with the human override study design. Human-reviewed AI workflows in SmartSolo are one operational example of that connection — they do not replace the ownership decisions this checklist asks you to make.