Research on Intelligent Traceability Framework for Trusted Artificial Intelligence

Qinmi Sun, Haibin Li

Abstract

With the deep integration of artificial intelligence and big data technology, the self-learning ability of the system brings efficiency improvement, but problems such as data pollution, algorithm black box, and model drift exacerbate the difficulty of tracing. This article proposes a three-layer traceability framework (TVB-Trace) that integrates blockchain metadata anchoring, dynamic verification mechanism, and trusted execution environment. By constructing a verifiable data lineage graph and algorithm decision chain throughout the entire lifecycle, it achieves transparent supervision of AI self-learning systems. Experiments have shown that this framework can improve data traceability accuracy to 99.2% and enhance model decision interpretability by over 40%. (Keywords: artificial intelligence traceability, blockchain, trusted computing, self-learning system).

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IMEKO-TC8-11-24-2025-045.pdf

DOI

10.21014/tc8-2025.045

IMEKO TC

TC8 - Traceability in Metrology

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