Definition
AI documentation requirements refer to the documentation an organization must maintain to support safe operation, accountability, and compliance. Depending on the context, this can include intended purpose, system description, data provenance, evaluation results, risk controls, and instructions for users.
Why it matters
- Proof: if you cannot document it, you usually cannot defend it.
- Operations: documentation supports incident response and change management.
- User safety: clear instructions and limits reduce misuse.
How it works
Define artifacts -> version them -> update on changes -> share with the right audienceGood documentation is living documentation: updated when models, data, or processes change.
Practical example
A provider maintains a technical file with intended use, known limitations, evaluation tests, and risk controls. Deployers maintain operational records (training, oversight procedures, and incident logs).
Common questions
Q: Isn’t documentation just paperwork?
A: It’s also a control. It forces clarity about scope, limits, and responsibilities.
Q: What breaks documentation fastest?
A: Untracked changes. Without versioning and ownership, docs become outdated and misleading.
Related terms
- Model Accountability — ownership and traceability
- Algorithmic Transparency — user-facing disclosures
- Data Ethics — provenance and responsible use
- AI Conformity Assessment — documentation used as evidence
References
Regulation (EU) 2024/1689 (EU AI Act).