All courses / AI Governance / Module 3 / Chapter 8
Learning objectives
After the lesson the participant can:
- Classify a use case with the six-step decision tree, citing the article behind every answer.
- Explain why the profiling question is asked separately from the Article 6(3) derogation.
- Draft a risk register entry that links a hazard to controls, assessments, an owner and review triggers.
- Describe how a versioned risk appetite statement becomes the threshold a deployment gate enforces.
Key concepts
- Classification decision tree
- Six questions in order: is it an AI system under Article 3(1), is it a prohibited practice under Article 5, is it high-risk under Article 6, does the Article 6(3) derogation apply, do Article 50 transparency duties apply, and is the organisation provider or deployer.
- AI Decision Record
- A version-controlled record, kept for every AI system, that states its intended purpose, user population, data, model, usage mode and the organisation's role, and that changes through reviewed commits. It serves as the use-case descriptor the policy evaluates.
- Excluded uses
- The list in a descriptor of uses the system must not be put to. Naming them makes drifting into them a visible change of intended purpose, which re-runs the classification.
- Risk register as code
- Each entry is a YAML document validated against a schema, reviewed through a pull request and linked by identifier to system, controls, evidence and assessments. Its review triggers name the events that force re-evaluation, and a stale entry counts as an open risk.
- Risk appetite as data
- The board-level declaration of acceptable risk expressed as rules per tier, with required assessments and a maximum residual rating. Versioned, it shows the appetite in force when a system was approved and lists the systems that no longer fit when it tightens.
Interactive exercises
Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.
Interactive exercise 1 · Put in order
The classification decision tree
Put the six steps of the chapter's decision tree for the AI Act classification in the order in which they are run.
Interactive exercise 2 · Sort
Which tier does the policy return?
Sort each use into the tier that the chapter's classification policy would return for it.
Teaching plan
| 10 min | Opening: the teacher asks when participants' AI risk assessments were last re-opened, then presents why a control once implemented does not stay implemented for AI. |
| 25 min | Teacher walks through the decision tree using Nordvik Region's risk-tiering questionnaire for benefits triage, then the staff assistant descriptor that stays limited-risk through its excluded uses. |
| 15 min | Presentation and questions on the register entry, the six classes of change that trigger re-evaluation, the substantial-modification rule and the appetite statement. |
| 30 min | Exercise in pairs: complete a tiering questionnaire for one system and write its register entry with triggers. |
| 10 min | Debrief: pairs challenge the basis of each other's answers, not the conclusion, and check the resulting tier against the appetite rules. |
Discussion
- Teams often describe a system as only prioritising. Why does the chapter treat that description with suspicion, and which of your systems would it apply to?
- The substantial-modification rule does not decide that a change is substantial; it decides that a human must make the judgement. What is gained by drawing the line between machine and legal judgement there?
- No committee meets at deployment, because its work happened when it approved the policy, the appetite and the legal interpretation. What would your risk committee have to give up, and what would it gain, to work that way?
Written exercise
Exercise · 30 minutes
Tier a use case and write its register entry
Choose an AI system from your own organisation, or a variation of Nordvik Region's staff assistant that is now proposed for recommending eligibility. First complete a risk-tiering questionnaire with the chapter's questions: is it an AI system, does any use fall under Article 5, is it used in an Annex III area, is the task only narrow or preparatory, does it profile natural persons, does it interact with people or generate content, what is your role, and are you a body governed by public law. Give the basis for each answer and state the resulting tier and obligations. Then write a register entry: hazard, harm categories, inherent rating, at least three controls, residual rating, owner, linked assessments, review period and triggers.
Deliverable: A completed tiering questionnaire with article references and resulting tier, and one register entry with controls, owner and review triggers.
Self-test
Choose an answer to see whether it is right, or open the answer below each question.
In Nordvik Region's questionnaire for the benefits-triage system, why can the Article 6(3) derogation not apply?
Show the answer
B. Because the system profiles natural persons, which excludes every derogation ground
The derogation never applies where the system profiles natural persons. Q6 is asked separately because this is the answer most often missed when a team describes its system as only prioritising.How does the chapter implement the requirement in Article 9 for regular review of risks?
Show the answer
D. As triggered review: a commit that changes model, data or purpose re-opens the entries
In an Architecture as Code practice, regular is implemented as triggered. The pipeline downgrades the entry to review required, assigns it to the owner and blocks promotion until the residual rating is reaffirmed or revised.What does the gate do with a register entry whose last review date plus review period has passed?
Show the answer
A. It treats the stale entry as an open risk
An entry past its review period is stale, and the gate treats stale entries as open risks. This mirrors the drift detection that Architecture as Code applies to infrastructure.