Traceability
The ability to track and document each step in the AI lifecycle—from data collection through model development to deployment—to support auditing and forensics.
Definition
Achieved via end-to-end lineage tracking: datasets are versioned, feature transformations are logged, training code commits are recorded, hyperparameters are stored, and deployment artifacts are tagged. Traceability systems enable reconstruction of exactly how and why a given prediction was produced, and facilitate root-cause analysis when issues arise. Governance requires that every pipeline stage emits provenance metadata to a central registry.
Real-World Example
A financial institution uses a metadata store that logs every dataset version, feature-engineering script, training run, and deployment tag. When a regulatory audit requests the history of a specific credit-score decision, the team retrieves the complete lineage—demonstrating full traceability.
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