Advisor on AI Decision Controls and Human Oversight

Observe the control in use

Follow representative cases through input preparation, model output, human review and final action. Assess what reviewers can see, whether they understand the limitations and whether they have time and authority to disagree. Investigate how ambiguous cases, missing information and unexpected outputs are handled. A high agreement rate may reflect model quality, reviewer dependence or an incentive to process work quickly; the analysis must distinguish these possibilities.

Evidence that should change a recommendation

Look for systematic error patterns, differences across relevant groups, repeated overrides and cases where a correct outcome occurs despite a weak process. Review how changes to data or the model are communicated to operators. Identify what evidence would justify narrowing, pausing or expanding use.

Design a workable control

Recommend intervention points, reviewer capability, escalation criteria and monitoring proportionate to the consequences of error. Consider whether a simpler rule or non-AI process would be more reliable. Advice must include the operating cost of meaningful review and the risk that users will bypass an impractical control.

**Candidate evidence.** Applicants should combine AI-risk understanding with experience designing or running decision processes. They must explain a situation where adding a human reviewer did not solve the underlying problem and how the design changed. Strong analytical communication and the ability to observe frontline work are essential.

Advisory accountability

The advisor will provide findings, options and a test plan; management retains deployment authority and responsibility for decisions. Legal and sector-specific requirements must be assessed by the appropriate specialists. The final output should make clear what the system is fit to support, what it should not decide and how the business will know when that judgement needs revisiting. Success is an operationally credible safeguard rather than a policy statement about human oversight.