Resources · Worked example 4

Model card

Worked example · fictional company (Vesta Mutual Insurance AS) · free PDF · CC BY 4.0

A model card is easy to fake by leaving things out. This one is built to do the opposite: the credibility comes from the findings a vendor brochure would hide.

PriceWise prices term life and health policies, which makes it high-risk, and Vesta is both its builder and its user. The card shows the amber items plainly. The youngest age band is thin in the training data and slightly under-priced, so its manual-review band is widened rather than tightened. Rural-county residents sit borderline on calibration and are flagged for the next retrain. Gender is excluded as an input, following Test-Achats, and a proxy-leakage test checks whether occupation and disclosed conditions reconstruct it anyway: the reconstruction score of 0.64 sits under the 0.70 action threshold and is recorded so the next test has a baseline. None of that is hidden in an appendix; it is the point of the document.

The oversight is specific, not aspirational. Underwriters see the top contributing factors with every score and can override with a recorded reason; the override rate is reviewed monthly, and a rate above 15% in any segment pauses the model there. A monthly drift dashboard, quarterly fairness tests, and an annual revalidation feed board reporting, and every retrained version gets a new card before it ships. The structure follows the Mitchell et al. model-card format, adapted for an insurer that is both provider and deployer under the AI Act.

Legal references: EU AI Act Annex III 5(c) (insurance pricing) · Article 27 (fundamental rights impact assessment). Card format: Mitchell et al., Model Cards for Model Reporting (2019).

What's inside

  • Model details, architecture, and regulatory status (provider and deployer)
  • Intended use, and the out-of-scope uses drawn explicitly
  • Training data, the excluded inputs, and the known data gaps
  • Performance by segment, with Gini and calibration explained
  • Fairness evaluation: proxy-leakage, distribution, and override-pattern tests
  • Limitations, drift monitoring, and the amber findings carried openly
  • Human oversight, monitoring cadence, and card maintenance

Download the PDF

Worked example for portfolio and training purposes. Vesta Mutual Insurance AS is a fictional company; all data, metrics, and names are invented. Prepared by Erik Bernath, Furioso AI Consulting OÜ. Licensed CC BY 4.0: reuse freely with attribution. Informational, not legal advice.

The other four examples

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