Evaluation of Counterfactual Fairness Audits for Artificial Intelligence Agriculture Credit-Scoring Models Under Ethnic Cooperative Lending Networks: A Governance Disparate-Impact Estimation Strategy

Artificial intelligence credit-scoring models increasingly mediate smallholder access to agricultural finance through ethnic cooperative lending networks, yet the fairness audits used to govern them treat group membership as an individual attribute rather than a network position. The argument proceeds in three steps. First, it shows that the standard counterfactual fairness criterion, which asks whether a decision would change under a hypothetical intervention on a protected attribute, is mis-specified when creditworthiness is produced through cooperative co-signing and peer monitoring, because the protected attribute enters the outcome through network structure rather than through the applicant alone. Second, it specifies a network-conditioned counterfactual that holds the lending network fixed while varying the applicant's ethnic classification, and derives the disparate-impact estimand that follows from it. Third, it characterises the operating characteristics of a before-after-control-impact design for estimating that estimand under a declared variance structure with an intraclass correlation of 0.64, a between-cluster standard deviation of 6.0 and a residual standard deviation of 4.5, and reports that 52 clusters are required to detect a 5 percentage-point effect at 80 per cent power. The contribution is a governance estimand, not an empirical finding: it specifies what a fairness audit of a network-embedded credit model should estimate, and the design under which that estimand can be recovered.