A housing authority AI audit timeline is set without assessing the team's need for statistical or model literacy. What planning gap should the auditor cite?

Citing inadequate resource and competency planning for AI audit skills works because housing AI evaluations often require statistical literacy, model documentation review, fairness metric interpretation, and data-lineage tracing that generalist auditors may not yet have. Timelines that ignore skills gaps produce superficial testing, over-reliance on vendor narratives, or missed material findings. Planning must match competency to the housing model’s risk profile—through training, specialists, or co-sourced expertise—before locking dates.

Delayed scheduling of model performance validation activities fails as a material planning gap relative to AI competency; postponing validation does not enable model evaluation if auditors lack the skills to interpret results.

Limited alignment of audit scope with tenant eligibility criteria fails conceptually by centering program eligibility rules instead of auditor capability.

Incomplete integration of fairness testing into fieldwork procedures fails because fieldwork procedure gaps do not substitute for statistical and AI audit skills needed to test housing decision systems.

Exam caveat: competency planning is not an excuse to defer all AI audits indefinitely—use targeted upskilling or specialist support for the highest-risk housing systems while building broader capability. Operational check: add a skills matrix to the housing AI audit plan (needed competencies vs available staff), record training or co-source actions, and delay fieldwork only where a documented competency gap would make conclusions unreliable.