Artificial Intelligence Governance of Outbreak Sentiment Surveillance Models Under Unmeasured Provincial Censorship Review Latency: A Healthcare Political Validity Evaluation

Outbreak sentiment surveillance models are increasingly used to inform public health communication, yet the political conditions under which their outputs are reviewed before release remain unmeasured in most governance frameworks. The central problem is that the estimand of interest—the effect of surveillance-informed communication on outbreak sentiment—is confounded by the review process itself, because provinces that review slowly differ systematically from those that review quickly in ways that are correlated with both the sentiment outcome and the timing of intervention. Using a before-after-control-impact design with a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), the paper characterises the operating characteristics of the design across 2000 replications per cell for cluster counts of 8, 16, 24, 32 and effect sizes of 3, 5 and 8 percentage points.54 clusters are required to detect a 5 percentage point effect at 80% power, and that the minimum detectable effect at 80% power declines as cluster count increases. The contribution is a design-based account of how review latency enters the causal estimand, together with a power analysis that specifies the cluster counts at which political validity claims become defensible. Governance of outbreak sentiment surveillance must treat review latency as a design parameter rather than an implementation detail, and it concludes that political validity evaluation requires explicit modelling of the review process before model outputs are treated as actionable evidence.