Enabled Data Provenance Framework for Transparent AI Model Training Pipelines

Abstract

AI systems rely heavily on high-quality training data, yet provenance tracking remains fragmented and vulnerable to manipulation. This study presents a blockchain-enabled data provenance framework designed to bring transparency and verifiability to AI training pipelines. The architecture records dataset lineage, preprocessing steps, annotation events and model updates using immutable smart contract transactions. A lightweight off-chain storage mechanism reduces blockchain overhead while maintaining audit guarantees. The system was tested with three machine learning pipelines involving image classification, text processing and sensor analytics. Results show a 95 percent reduction in provenance disputes and full traceability across all data contributors. Smart contracts automate compliance checks and access permissions, ensuring that only validated datasets feed into the training process. The framework improves accountability for AI ethics, model bias evaluation and regulatory reporting. Experiments confirm that blockchain latency does not significantly affect pipeline throughput due to parallelized validation nodes. The work demonstrates how decentralized technologies can support trustworthy AI development. Future research will explore integration with zero-knowledge proofs to further enhance confidentiality.