Decision Traceability is a record of what was decided, by what, on what basis, and who is accountable.
Decision traceability is the record of the decision itself. It captures what was recommended, by which human or AI system, on what basis, who reviewed it, and on what authority it was acted on. Traceability is what separates a defensible decision from an unaccountable one.
The discipline matters most when an AI recommendation is accepted with little human change. Without a trace, the organization cannot show that a human stayed accountable. With a trace, it can prove who decided and why, which is exactly what regulators and courts ask for.
Traceability maps to the NIST AI RMF Govern and Manage functions and to the record-keeping and human-oversight requirements of the EU AI Act.
References
- NIST AI Risk Management Framework (AI RMF 1.0): Govern, Map, Measure, Manage. National Institute of Standards and Technology, 2023. View source ↗
- Information governance: the records and data lifecycle discipline (storage, retention, disposition), distinct from AI decision governance. ARMA International, Generally Accepted Recordkeeping Principles; AIIM. View source ↗
- EU AI Act, Regulation (EU) 2024/1689 (Official Journal of the European Union); ISO/IEC 42001:2023; Texas Responsible AI Governance Act (TRAIGA). View source ↗
- USPTO Trademark Reg. No. 99559923, Digital Information Governance / DIG, owner Matthew Bertram. View source ↗