Blockchain Timestamping for AI Model Lifecycle Tracking
DOI:
https://doi.org/10.63345/Keywords:
Blockchain Timestamping, AI Lifecycle, Provenance, Merkle Trees, Content Addressing, W3C PROV, Verifiable Credentials, MLflow, DVCAbstract
The accelerating complexity and societal impact of artificial intelligence (AI) systems have sharpened calls for reproducibility, auditability, and trustworthy governance of the entire model lifecycle—from data collection and labeling to training, evaluation, deployment, monitoring, and retirement. This paper proposes blockchain‐anchored timestamping as a practical, standards-aligned mechanism to create tamper-evident, independently verifiable evidence of when specific lifecycle events occurred and what artifacts (data snapshots, model checkpoints, code commits, configuration files, and deployment manifests) were involved. Building on the cryptographic foundations of secure hashing, Merkle aggregation, and append-only ledgers, we integrate content-addressed storage and well-known provenance schemas to produce minimal-disclosure receipts that reduce storage cost and leakage risks while preserving verifiability.
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