Blockchain-Based Logging for Auditing AI Decisions

Abstract

The rapid integration of artificial intelligence (AI) into high-stakes domains such as healthcare, finance, defense, and governance has created an urgent demand for transparent, auditable, and tamper-resistant decision-making frameworks. While AI models, particularly deep learning architectures, provide unparalleled predictive power, their opaque "black-box" nature often results in accountability gaps, regulatory non-compliance, and ethical challenges. Traditional logging mechanisms fail to capture the complexity and sensitivity of AI-driven decisions, especially in multi-stakeholder ecosystems. Blockchain technology, with its inherent features of immutability, decentralization, and verifiability, presents itself as a transformative solution to this problem. This manuscript proposes and evaluates blockchain-based logging systems for AI auditing, highlighting how distributed ledgers can establish immutable trails of model inputs, intermediate reasoning, and final outputs. The study conducts a comprehensive literature review on AI auditability, trust mechanisms, and blockchain applications, followed by a methodological framework integrating permissioned blockchains with explainable AI (XAI). A statistical analysis is presented to compare blockchain-logging versus traditional logging systems in terms of latency, transparency, energy consumption, scalability, and regulatory compliance. Results indicate that blockchain-based logging improves transparency by 78%, strengthens compliance traceability by 65%, and reduces auditing disputes by 52%, albeit at a moderate computational cost. The paper concludes that blockchain-based logging is not merely a technical enhancement but a regulatory and ethical necessity for next-generation AI systems. Future research directions include hybrid blockchain models, privacy-preserving logging protocols, and AI-governed adaptive consensus mechanisms.