Evaluation of Contestable-Explanation Sufficiency for Artificial Intelligence Agriculture Loan Denials Under Appellate Bureaucratic Review: A Governance Reversal-Rate Estimation Strategy

Agricultural credit algorithms that deny loans must supply explanations that smallholders can contest before appellate bureaucrats. the present analysis treats the sufficiency of those explanations as a governance parameter rather than a technical property, and proposes a reversal-rate estimation strategy for evaluating it. The design is a before-after-control-impact (BACI) comparison in which appellate panels are observed before and after the introduction of a contestable-explanation standard, with untreated panels providing the counterfactual trend. Because no observational data are used, the paper characterises the operating characteristics of the estimator 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 replications per design cell for 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 shift in reversal rates at 80 per cent power. Reversal-rate estimation converts explanation sufficiency from an interpretive claim into a measurable governance quantity, and it specifies the institutional conditions under which that quantity can be trusted.