As AI systems are increasingly used to support or automate decisions, organisations need clear and accessible records that explain what the system does, how it was designed, and what limitations or risks are known. This is especially important when AI is used to make or influence decisions that affect people, require human oversight, are reviewed externally, or may change over time.
To support transparency and accountability, this practice introduces consistent documentation standards for how AI systems work, what data they use, and how key decisions are made. This includes key details about the data, such as source, quality, and limitations, as these directly affect system behaviour and decision outcomes.
It also covers how the system or model functions and how it supports or influences decisions so teams understand the technology and its role in the real world. Standardising documentation helps reduce ambiguity across teams and enables better governance, validation, and monitoring, whether systems are developed in-house or externally sourced.
If your organisation only procures AI systems, the focus is on ensuring suppliers can provide sufficient documentation, such as model cards, interface descriptions, or system behaviour summaries, that align with your internal governance needs.
Why it matters
This practice builds on AI Ready foundations, including system inventory and human oversight. Those practices helped teams identify where AI is used and define when people should stay involved.
Now, you are moving to consistent documentation that supports ongoing governance, improves explainability, and ensures oversight is based on a clear understanding of how each system works.
Without consistent documentation, it becomes difficult to explain, challenge, or improve how AI systems are used, increasing the risk of poor accountability, unfair outcomes, or performance issues.
Standardised documentation helps address this by making system logic more transparent, supporting cross-functional collaboration, and ensuring AI systems are understood, governed, and ready for review if something goes wrong.
Implementation tips
- Start with a simple template that includes: system purpose, data inputs, model or rule logic, known limitations, and human oversight points.
- Align documentation depth to system risk, with high-impact models may require more technical detail, review history, or fairness evaluation results.
- If you use third-party AI, request documentation that aligns with your internal templates (e.g. model or system cards).
- Make documentation easy to update and store it in a shared location or register.
- Link documentation to your approval and risk processes instead of treating it as a one-off artefact.
- Provide examples or pre-filled templates to reduce the burden on delivery teams.
Support materials
ICO & Turing Institute – Explaining decisions made with AI
Guidance to help organisations explain AI-assisted decisions clearly and meaningfully.
NZ Ministry of Social Development – Data Science Guide for Operations
A practical guide to help teams document how and why machine learning models support.
MAS – AI Model Risk Management Guide
Offers risk-tiered documentation guidance and practical examples for ensuring auditability and explainability across different governance contexts.
Google – Model Cards for Model Reporting
A structured template for documenting model purpose, inputs, outputs, risks, and performance.
Microsoft – Data Sheets for Datasets
A companion to model cards, focused on transparency and quality of dataset inputs.
