Artificial Intelligence Governance of Vaccine Prioritization Models Under Unmeasured District Health Committee Rationing Latitude: A Political Fairness Evaluation

District health committees in decentralised health systems routinely exercise rationing latitude that is not captured in the data used to train and audit vaccine prioritisation models. the present analysis evaluates the political fairness consequences of that unmeasured discretion through a replication-based design analysis of the before-after-control-impact estimator, using a declared variance structure with an intraclass correlation of 0.64, a between-cluster standard deviation of 6.0 and a residual standard deviation of 4.5. Power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points.54 clusters are required to detect a 5 percentage point effect at 80 per cent power, a requirement that exceeds the cluster counts typically available to district-level governance evaluations. The political fairness of vaccine prioritisation models cannot be assessed through model performance metrics alone, because committee latitude operates as a structural confounder that the before-after-control-impact design can only partially absorb. A governance framework is proposed that treats committee latitude as a measurable political variable rather than an unmodelled residual, and the implications for algorithmic accountability in decentralised health systems are examined.