testland/model-risk-evidence-matrix
Assigns a machine learning model to a low, medium, or high risk tier from what its predictions decide about people, then derives the fairness and explainability evidence that tier must produce: group metrics per declared sensitive feature, intersectional breakdowns with per-cell counts, vulnerability scan categories, a drift monitoring plan, and per-prediction explanation logs. Supplies conventional demographic parity difference bands, a per-vulnerability-category blocking table, and evidence rules that mark a bundle incomplete or self-contradicting. Use when a model release candidate is up for promotion and someone must decide which fairness artifacts are mandatory rather than nice to have, or when a model card declares a risk tier and the attached evidence bundle has to be checked against what that tier demands.
80
100%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
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Passed
No findings from the security scan
worked-example.mdreferences/
Worked example - model-risk-evidence-matrix
A walk-through of a mis-tiered credit model checked against the evidence matrix. Step numbers and rule IDs (R2, R4, R6) refer back to SKILL.md.
Input. Model card declares: consumer credit approval scoring, tier stated as
medium, sensitive features ["sex", "race"], "monitored in production". Bundle
contains aggregate metrics, a group fairness table on sex and on race, and a
vulnerability scan.
Step 1. The stated tier is wrong. Credit approval decides about an individual in credit, and Regulation B requires specific principal reasons for an adverse action (12 CFR 1002.9). Re-tier to high. Every subsequent step uses the high column.
Step 2 gap list. Intersectional table: absent. Per-prediction explanation logs: absent. Drift monitoring plan: claimed, artifact not present.
Step 3. The bundle reports DPD only. That is a defensible choice for credit selection, but the model card gives no reason for it, so the choice itself is recorded as a gap.
Step 5. DPD on sex is 0.087, landing in the REVIEW band, so a written
justification plus a monitoring plan must be present. DPD on race is 0.04,
PASS.
Steps 6 and 8. R6 fires (no intersectional table). R2 fires (no explanation logs). R4 fires (production monitoring claimed, no schedule or reference dataset).
Output.
## Evidence coverage - `credit-approval-v4`
Tier as declared: medium
Tier as assessed: HIGH (individual credit decision; adverse-action reasons required)
Sensitive features declared: sex, race
Bundle status: INCOMPLETE (3 rules fired)
| Required artifact (high tier) | Present | Note |
|---|---|---|
| Performance metrics | yes | accuracy 0.86, AUC 0.89 |
| Group fairness: sex | yes | DPD 0.087 -> REVIEW band, justification missing |
| Group fairness: race | yes | DPD 0.04 -> PASS |
| Intersectional (sex x race, with counts) | no | R6 |
| Vulnerability scan | yes | 0 blocking categories, 2 underconfidence findings (advisory) |
| Drift monitoring plan | claimed | R4: no schedule, no reference dataset |
| Per-prediction explanation logs | no | R2 |
| Mitigation provenance (DPD > 0) | no | required once a disparity is non-zero |
### Rules fired
- R2 - high tier, no local explanation logging.
- R4 - "monitored in production" asserted with no reference dataset or schedule.
- R6 - no intersectional table across the two declared sensitive features.
### Unowned decisions
- No named owner recorded for the 0.05 / 0.10 DPD bands.
- No recorded reason for choosing demographic parity over equalized odds.
### To close the bundle
1. Produce an intersectional table across sex x race including a per-cell count column.
2. Log local explanations for at least one positive and one negative predicted class.
3. Attach the drift schedule plus the named reference dataset, or drop the monitoring claim.
4. Attach mitigation provenance or a four-field waiver for the 0.087 DPD on sex.
5. Record the band owner and the metric-choice rationale in the model card.The output never says the model is fair or unfair. It says which required evidence exists and where the bundle contradicts itself.