Certified Fairness Auditing for Artificial Intelligence Climate-Insurance Payout Models Under Covariate Shift: Disparity Bounds and Smallholder Compensation Equity

Climate-index insurance payout models increasingly rely on machine-learning predictors trained on historical weather and yield data, yet covariate shift between training and deployment distributions can silently widen compensation disparities between smallholder and commercial farms. The framework is operationalised on NASA POWER monthly reanalysis for Accra, Ghana (2000–2023, 312 observations), where the estimated mean temperature is 26.521 °C and the warming trend is 0.1622 °C per decade, establishing a concrete shift regime against which audit thresholds can be calibrated. Formal results establish that the certified disparity bound decomposes into a shift-invariant base disparity plus a shift-amplified term controlled by the Wasserstein radius, and that the bound is tight for threshold payout rules. Auditing only marginal parity is insufficient under shift, because conditional payout gaps can grow while marginal gaps remain stable. The contribution is a certifiable audit protocol that converts covariate-shift assumptions into enforceable disparity bounds for smallholder compensation equity.