Trust Alignment Mechanisms for Distributed AI Operating Across Jurisdictional Data Zones

Abstract

Distributed AI systems increasingly operate across multiple jurisdictions, each governed by distinct regulatory expectations for transparency, accountability, and human oversight. As inference nodes diverge in calibration, explanation formatting, and uncertainty disclosure, trust behavior can vary even when the underlying model remains synchronized. This study evaluates trust calibration mechanisms across multi-jurisdiction data zones by simulating distributed inference nodes, monitoring trust signal dynamics, and assessing adaptive explanation and confidence adjustments within real workflow contexts. The results show that trust is not a static model property but an operational behavior influenced by regional policy constraints, synchronization patterns, and domain-specific usage. Systems that employ periodic cross-node harmonization and context-sensitive trust shaping maintain both interpretive alignment and user confidence. The findings emphasize the need for adaptive governance frameworks that treat trust calibration as a continuous process rather than a one-time compliance event.