AI risk classification and governance

What it is

Sorts a candidate use of AI into a regulatory risk tier — broadly, unacceptable, high, limited or minimal — before it is built, using the framework the EU AI Act put into force and the ethical failure modes that sit underneath it regardless of jurisdiction: bias, opacity, privacy and misinformation. The tier decides the obligations, so classifying comes before scoping.

The four tiers

Unacceptable uses are prohibited outright. High-risk uses — most hiring, credit, and safety-relevant systems — carry documented obligations before deployment. Limited-risk uses need disclosure. Minimal-risk uses carry no obligation beyond ordinary good practice.

Classification reasoning

Why this specific use sits where it sits, in terms a non-specialist reader can follow. A tier assigned without shown reasoning cannot be checked or challenged later.

Bias and fairness exposure

Whether the training data or the decision the system makes could produce a systematically unfair outcome for a protected group, and what has been done to check.

Explainability

Whether the people affected by a decision, and the people accountable for it, can actually understand why the system reached it. A high-risk use with no explainability story is a live liability, not a technicality.

Data governance

Where the data came from, what it was permitted to be used for, and whether that permission covers this use.

Named ownership

Who is accountable for this classification and for revisiting it if the use case changes. An unowned classification decays the moment the product does.

How you run it

1. Write the use case as a specific decision, not a categoryWhat the system actually decides or recommends, for whom, and what happens as a result. 'AI in HR' is not classifiable; 'AI that ranks CVs for shortlisting' is.
2. Establish the current regulatory position firstThe tiers, thresholds and obligations that apply in every jurisdiction the business operates or sells into. Regulation is moving; confirm rather than assume.
3. Assign the tier and write the reasoning downWhich tier, and the specific features of the use case that put it there. A reader should be able to check the classification against the reasoning.
4. Check bias, explainability and data governance for anything above minimalThe three failure modes that sit under most obligations regardless of the exact regulatory tier.
5. Name an owner and a revisit triggerWho is accountable, and what change to the use case — new data source, new decision, new geography — would require reclassifying it.

The prompt

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