Distributed AI Trust Calibration Across Multi-Jurisdiction 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.