Federated Trust Models for AI-Driven Decision Automation: Evidence from Healthcare and Other Regulated Industries

Abstract

Artificial intelligence systems increasingly automate critical decisions in regulated industries, yet stakeholder trust remains fragile due to concerns about transparency, accountability, and algorithmic bias. This research develops and evaluates federated trust models that enable collaborative AI decision-making across organizational boundaries while maintaining institutional autonomy and regulatory compliance. The study implements trust frameworks in healthcare clinical decision support, financial credit approval, and pharmaceutical supply chain management, involving 47 participating organizations across three industries. The federated approach achieved 87% stakeholder trust scores compared to 54% for centralized AI systems, while maintaining decision accuracy of 91.3%. Trust verification mechanisms detected and prevented 96% of malicious model contributions through Byzantine-robust aggregation and cryptographic validation. The framework reduced regulatory compliance costs by 62% through automated audit trails and explainable decision provenance. Privacy-preserving federated learning enabled model improvement across institutions without sharing sensitive data, achieving 89.4% of centralized model performance while maintaining local data sovereignty. Healthcare applications demonstrated 34% improvement in clinical decision support adoption rates when trust mechanisms provided transparent reasoning and institutional validation. This research contributes practical federated trust architectures, empirical evidence of trust's impact on AI adoption in regulated settings, and implementation guidance for organizations deploying collaborative AI systems.