Someone may sign an automated result without time or authority to change it. That does not make review effective or provide meaningful appeal.
The decision to examine
Define who can reject a result, what evidence they see, when processing stops and how users appeal. Track missed errors, not merely signatures.
Worked example
In hypothetical request triage, an officer reviews ambiguous cases and can change categories. The final decision remains theirs; our exercise logs correction reasons without sensitive data.
From idea to practice
- Describe the problem as a need, not a product name.
- Specify evidence needed before a decision and who reviews it.
- Test a non-personal example and log failures as well as successes.
- State whether to continue, improve or stop and why.
Your exercise
Write one sentence identifying stopping authority and one explaining appeal. If either is unclear, redesign the workflow.
Review rubric
A useful answer identifies a context, checkable evidence, a boundary and an owner for the next step. If it fits any organisation unchanged, add a detail specific to the case. This is a learning rubric, not automatic marking or official assessment.
Further reading: AI Risk Management Framework — NIST · 2023; page reviewed 2026 · Checked: 8 October 2026
Your next step
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Institutional DJAIRI material prepared with AI assistance. Learning examples are hypothetical; analysis and recommendations are Mustanef’s. Citing a source does not imply partnership or endorsement.