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September 10, 2025

Chain-of-Trust AI: Zero-Knowledge Verified Federated Reinforcement and Generative Learning for Interpretable, Bias-Free Decision-Making in Decentralized Complex Systems

Key Points

The Chain-of-Trust AI framework enables interpretable and bias-free decision-making in decentralized systems.
Integrating zero-knowledge proofs prevents bias propagation and guarantees transparency in model updates.
Methodological evaluation through multi-agent simulations assesses interpretability, fairness, and verification overhead.
Applications in healthcare, fraud detection, and autonomous systems highlight the framework's robustness and scalability.

Abstract

The rapid growth of artificial intelligence (AI) in decentralized systems such as healthcare, financial networks, and autonomous transportation has underscored the critical need for interpretability, fairness, and verifiable trust in decision-making. Traditional federated learning frameworks, while addressing data privacy and scalability, often suffer from bias propagation, opaque model behaviors, and limited mechanisms for ensuring accountability. This article introduces Chain-of-Trust AI, a novel paradigm that integrates zero-knowledge proofs (ZKPs), federated reinforcement learning (FRL), and generative learning models to create an interpretable, bias-free, and verifiable decision-making framework for complex distributed environments. The proposed framework leverages FRL to enable adaptive coordination across heterogeneous agents while maintaining local data sovereignty. Generative learning models, such as variational autoencoders, provide transparent causal representations that support bias detection and enhance interpretability of reinforcement-driven policies. ZKPs are embedded as cryptographic guarantees to verify model updates and decision outcomes without exposing sensitive information, thus ensuring compliance, trust, and transparency across decentralized networks. Methodologically, the framework is evaluated through MATLAB-based multi-agent simulations, benchmarking performance in terms of interpretability, fairness indices, convergence stability, and verification overhead. Theoretical analyses confirm convergence under heterogeneous reward structures, cryptographic soundness of proofs, and bias reduction capabilities through generative regularization. Case studies in decentralized healthcare diagnostics, financial fraud detection, and autonomous vehicular coordination highlight the practical scalability and robustness of Chain-of-Trust AI. By uniting reinforcement learning, generative interpretability, and zero-knowledge verification, this work pioneers a secure, auditable, and ethically aligned AI architecture for decentralized complex systems, advancing both technical rigor and governance in distributed intelligence.

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