Traceability
Being able to follow the trail, from an AI output back to the data, model, and decisions that produced it, to understand exactly how a result was reached.
The Simple Version
Being able to follow the trail, from an AI output back to the data, model, and decisions that produced it, to understand exactly how a result was reached.
Detailed Explanation
AI traceability encompasses data traceability (linking model behaviour to training data), model traceability (versioning models and linking to experiments), decision traceability (recording individual AI-assisted decisions with context), and process traceability (documenting workflows and human interventions). The EU AI Act requires traceability through its logging requirements (Article 12) for high-risk systems and data governance documentation in Annex IV. W3C PROV-O provides a semantic vocabulary for representing traceability. ISO/IEC 42001 includes traceability as a management system requirement.
Key Characteristics
- Spans data, model, decision, and process traceability dimensions
- Required by EU AI Act Article 12 logging requirements for high-risk systems
- Enables post-incident investigation and bias root-cause analysis
- Can be represented using W3C PROV-O standard
Why It Matters
MLOps platforms that enforce experiment tracking, model versioning, and decision logging create traceability as a platform capability, reducing the governance burden on individual teams and ensuring consistent auditability.
Real-World Analogy
Like the traceability system in the food supply chain, every step from farm to table is recorded so that in the event of a contamination incident, authorities can identify the exact source and recall only the affected products.
Common Misconceptions
- Traceability is only relevant after an incident, proactive traceability enables faster investigation when incidents occur but is equally valuable for routine governance and improvement.
- Version control achieves full traceability, version control tracks code changes; complete traceability also requires data versioning, experiment tracking, and decision logging.
Related Terms
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