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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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  • DOI:
    tc8-2025.045

Event details:

  • IMEKO TC:
    TC8
  • Event name:
    IMEKO TC8, TC11 and TC24 Conference
  • Title:

    Joint conference of the TCs ‘Traceability in Metrology’ (IMEKO TC8), ‘Measurement in Testing, Inspection and Certification’ (IMEKO TC11), and ‘Chemical Measurements’ (IMEKO TC24).

  • Place:
    Torino, ITALY
  • Time:
    14 September 2025 - 17 September 2025