AI Decision Records, Model Cards and the Architecture Repository

All courses / AI Platform and Security / Module 1 / Chapter 5

Learning objectives

After the lesson the participant can:

• Explain why the Architecture Decision Record is not sufficient for AI systems and which fields the AI Decision Record adds.
• Draft an AI Decision Record with options considered, evaluation criteria, human oversight and retirement criteria.
• Distinguish the model card, the datasheet and the system card and say what each documents.
• Map the paths of an architecture repository to the points of Annex IV.

Key concepts

AI Decision Record (AIDR)

A record that keeps the short, numbered, immutable structure of the Architecture Decision Record and adds intended purpose, prohibited uses, risk classification, role, data sources, evaluation criteria, human oversight, logging and retention, and retirement criteria.

Retirement criteria

The conditions under which the system is withdrawn or the model retrained. They are the contract that lets evidence re-open a record: when a monitored threshold is breached, the pipeline creates a review task against it.

Model card and datasheet

Version-controlled companions of the record. The card reports a model's intended use, evaluation and limitations; the datasheet documents a dataset. The pipeline fails if the card's reported metrics do not match those the evaluation stage produced.

System card

The description of a compound system as a whole: every component and version, the data flow, system-level evaluation results and the controls. It records that the organisation deploys the general-purpose AI model but provides the compound system it has assembled.

Architecture repository

The single source of truth in which each Annex IV heading has one place where its evidence lives, so that the technical documentation is generated, versioned and signed on every model promotion and every merge to the decision log.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Teaching plan

Discussion

1. The retirement criteria of AIDR-0027 are deliberately asymmetric. What does that asymmetry say about the difference between a control that has failed and a system that performs poorly?
2. Nordvik Region shows caseworkers a priority band with reasons instead of a numeric score, and queues a random sample without a band. How would you show that human oversight in one of your systems is effective and not nominal?
3. Which facts about your AI systems are written by hand in more than one document today, and which of those documents would contradict each other first?

Written exercise

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

1. In AIDR-0011, why did Nordvik Region reject showing caseworkers a numeric score for each claim?

   1. Because a numeric score invites over-reliance
   2. Because the provider's model cannot output a score
   3. Because scores are special-category data under the GDPR
   4. Because a score would make the system fall outside Annex III
   Show the answer

   A. Because a numeric score invites over-reliance
   The chosen design shows a priority band with the three main reasons; the band rather than a score reduces automation bias.

2. How is an accepted AI Decision Record changed when the decision no longer holds?

   1. Its body is edited in place and the date of the edit is added to the front matter
   2. It is deleted from the repository once the retired model has left serving
   3. It is rewritten by the generator that produces the decision log index
   4. Only its status fields change, in the pull request that accepts a successor stating why it was retired
   Show the answer

   D. Only its status fields change, in the pull request that accepts a successor stating why it was retired
   The body of an accepted record is never edited. Supersession is the only way to change a decision, and the successor carries the reasoning.

3. What role analysis does the chapter say is recorded in the system card of a compound system built on a third-party language model?

   1. The organisation is the provider of both the model and the compound system, because it wrote the prompts and the retrieval configuration
   2. The model vendor is the provider of the compound system and the organisation its distributor
   3. The organisation deploys the general-purpose AI model but is the provider of the compound system it has assembled
   4. The organisation is a deployer of both, since it did not train the model
   Show the answer

   C. The organisation deploys the general-purpose AI model but is the provider of the compound system it has assembled
   The system card is where the roles are decided: if the compound system is high-risk, the provider obligations of Article 16 apply to the organisation, not to the model vendor.