Evaluation of Reprogrammable Prediction-Substitution Audits for Artificial Intelligence Agriculture Subsidy Models Under Post-Deployment Retraining: A Governance Drift-Detection Bound for Smallholder Eligibility Stability

Artificial intelligence models that determine smallholder subsidy eligibility are retrained after deployment, and retraining can silently alter which farmers are predicted eligible. the present analysis evaluates a governance instrument, the reprogrammable prediction-substitution audit, and derives a drift-detection bound for it. The audit substitutes model predictions for observed eligibility outcomes in a before-after-control-impact design, so that the estimand is the change in prediction-substitution error attributable to retraining rather than to secular change. Because no observational data are used or claimed, the analysis characterises the operating characteristics of the design under a declared variance structure: between-cluster standard deviation 6.0, residual standard deviation 4.5, and intraclass correlation 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design requires 54 clusters to detect a 5 percentage point drift at 80 per cent power. The audit is a defensible governance instrument only when its detection bound is stated in advance and treated as a condition of deployment, and it specifies the institutional commitments that this condition entails for subsidy agencies operating under post-deployment retraining.