What should a reviewer be able to follow after an AI-assisted decision?
A screenshot can explain an interface. A decision record should explain the source, proposal, authority, human decision and outcome. Use these questions when evaluating a workflow.
Source and purpose
Which input was used, where did it come from and what was the AI-supported step meant to achieve? Keep the operational purpose with the source context.
Recommendation context
What was proposed, by which model or agent context, and what supporting information was shown to the reviewer? Do not confuse a recommendation with an approved action.
Policy and authority
Which rule or threshold applied, and who was entitled to approve? Ask what the system does when no person approves or a proposal falls outside the boundary.
Human decision
Who reviewed the proposal, what did they decide and why? A rejection or override can be as important to retain as an approval.
Action and outcome
What was actually carried out and what result was observed? Discuss access, retention and export requirements with the people who will use the record.
Apply it to a specific workflow
A safety event needs visual and supervisor context. A carbon figure needs source, method and review context. The fields have a common structure, but the work differs.
Inspect the fictional sample record, then ask which fields and exports are available for the scope you are evaluating.
Questions to bring to the team
- Who owns an unresolved exception?
- What happens when input data is missing?
- Where does a person approve or reject the proposal?
- What record can a later reviewer actually inspect?