Traceability
Audit logs and provenance enabling system review and forensics.
Definitions (4)
The maintenance of audit logs, data provenance records, model documentation and change histories that enable reconstruction of system inputs, design choices and decision paths for accountability, audits and post‑deployment evaluation. Traceability supports testing, impact assessment and remedial action by ensuring system actions can be traced to sources and processes.
The requirement to maintain traceable data lineage, comprehensive documentation, model documentation and testing records to enable audit, oversight, investigation, and redress, supporting internal audit, ministerial inquiry and independent review.
The requirement to maintain provenance and audit trails for datasets, model versions, training processes, and decision logs so that the origins, development history, and operational behavior of an AI system can be reconstructed and audited over its lifecycle.
Traceability refers to the capability to follow the entire lifecycle of AI-generated content, from its initial creation to its dissemination. This is crucial for establishing accountability, verifying authenticity, and combating the spread of deceptive synthetic media.
Related Terms
Trazabilidad (traceability)
Audit logs and provenance records for AI systems and data....
data use audit trail
Automated logging of dataset access and processing activities....
Auditeability
Ability of a system to be independently reviewed and audited....
Audit Trail
Immutable logs of inputs, prompts, outputs and system versions....
Auditability of the AI system
Capacity to evaluate algorithms, data, and design or undergo audits....