Evaluation of Cross-Border Data-Sharing Consent Audits for Artificial Intelligence Agriculture Disease-Surveillance Models Under Regional Bloc Sovereignty Conflicts: A Governance Privacy-Commons Bound

Cross-border data sharing for artificial intelligence (AI) agriculture disease-surveillance models is constrained by a structural conflict between regional bloc sovereignty claims and the privacy commons on which surveillance depends. The bound specifies the conditions under which an audit regime can simultaneously satisfy bloc-level data-sovereignty constraints and preserve the statistical validity of pooled surveillance models. The argument proceeds through three analytical moves: a formalisation of the consent-audit problem as a constrained optimisation over privacy-loss budgets and model-validity thresholds; a derivation of the bound under regional bloc sovereignty conflicts, showing that audit stringency and model coverage trade off along a frontier whose curvature is set by the heterogeneity of consent regimes across blocs; and an evaluation of three audit architectures—centralised, mutual-recognition, and federated—against the bound. Mutual-recognition audits dominate when consent-regime heterogeneity is low, federated audits dominate when it is high, and centralised audits are dominated in both cases except where a single bloc holds agenda-setting power. The contribution is a governance-theoretic bound that converts an open policy dispute into a tractable design problem for consent-audit institutions in AI agriculture disease surveillance.