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Human review is a workflow with responsibilities, information and consequences. Adding an approval button does not explain what the reviewer is expected to assess or whether they can challenge the proposed result. Start with the decision and design the review around the person who must make it.
Specify the decision the reviewer owns
Describe what the AI proposes and what happens if the proposal is accepted. Identify the person or role authorised to make that decision. Distinguish checking a draft for clarity from approving an action that changes a record or affects another person.
Define the choices available: accept, amend, reject, request more information or escalate. Explain what each choice does and which ones require further permission. The application should enforce those responsibilities rather than relying on a prompt to describe who may approve an action.
Provide the information needed to review
Show the proposal alongside the material needed to assess it. Where the task uses retrieved information, make relevant sources available to the authorised reviewer. Distinguish the source material from the system's interpretation so the reviewer can identify unsupported conclusions.
Make missing information and known limitations visible. Avoid presenting a numerical confidence value unless its meaning and usefulness have been established for the task. A reviewer needs evidence that helps them decide, not a reassuring-looking signal that they cannot interpret.
Design for correction and disagreement
Let the reviewer inspect and edit the relevant details before committing. Preserve their work if a recoverable error occurs. Make the final action clear and show confirmation only after the responsible backend accepts it.
Consider what happens when information changes during review or two people act on the same item. Define how the application detects a stale proposal and avoids duplicate actions. A reviewer should not unknowingly approve a result based on data that no longer represents the current case.
Plan the review workload
Estimate the number of items requiring attention and the time available to the reviewers. Decide how work is assigned, prioritised and escalated when nobody can respond. Record the operating assumption and test it during a controlled trial.
Identify the skills and context the reviewer needs. Provide guidance and examples suited to the actual decision. If the queue cannot be handled as planned, revisit the scope or operating arrangement rather than assuming that a nominal human checkpoint will continue to provide meaningful oversight.
Test the complete review journey
Use representative correct, incorrect and incomplete proposals. Observe whether reviewers can find the relevant evidence, identify problems and use the available choices. Include keyboard access and assistive-technology needs in the interface review.
Check the downstream result as well as the screen interaction. Verify that rejection prevents the intended action, amendment uses the reviewed values and approval records the accepted outcome. Keep automated checks and observed reviewer performance as separate evidence.
Use review outcomes to improve the system
Record the information necessary to understand recurring issues while respecting the sensitivity of the material. Separate operational reporting from raw case content. Decide who may inspect detailed records and how long those records are needed.
Review correction themes and unresolved cases with the product and operating owners. Feed useful examples into the evaluation process and revisit the review design when the task changes. Human involvement should remain a demonstrable part of the decision process throughout the service's life.
An approval journey that survives a changed record
A fictional assistant proposes an amendment to a service request. Show the reviewer the original request, proposed values, relevant source and the record version used. Offer accept, edit, reject and escalation. Make the consequence explicit: acceptance submits an amendment to the responsible service; it is not merely a positive rating of the draft.
While the proposal waits, another authorised user changes the request. Approval must detect the stale version and ask for a fresh comparison rather than silently applying old values. Test rejection to prove no amendment occurs; test an edit to prove the reviewed values are used; test a double click and lost response to prove one accepted amendment is reconciled. A success label appears only after the service confirms acceptance.
Plan reviewer capacity with invented trial inputs: 80 items a day at an observed two minutes per item would require 160 minutes of review, before escalation and interruptions. Measure the actual trial rather than turning that illustration into a staffing promise. If the queue exceeds capacity, narrow use or adjust ownership. NIST’s framework supports considering human roles and risk; it does not prove that adding a reviewer makes this journey effective.
How this relates to Veda Software’s work
Powerleague’s venue and central operating roles are relevant to separating responsibilities. The case does not establish this AI approval flow. Veda Software handles the application journey, while VEDA AI owns strategy and adoption.