Mechanism-Design Auditing for Artificial Intelligence Agricultural Credit Scoring Under Temporal Distribution Shift: A Computer Counterfactual Stability Evaluation

Agricultural credit scoring models built on artificial intelligence are increasingly used to allocate seasonal finance, yet their performance is typically audited only against held-out samples drawn from the same period as training. The audit combines a counterfactual stability criterion with a before-after-control-impact (BACI) evaluation design, and characterises the operating characteristics of that design under a declared variance structure. Using a between-cluster standard deviation of 6.0, a residual standard deviation of 4.5 and an intraclass correlation of 0.640, power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design required 54 clusters to detect a 5 percentage point effect at 80 per cent power. It argues that stability auditing is a design problem before it is a measurement problem, and that the cluster count required for credible audit is substantially larger than the cluster counts typical of agricultural credit pilots.