fix(ms-ai-architect): G7 idx 26 lukket — scorecard-segmenter rettet, dashboard-komponenter merket

Error analysis og Counterfactual analysis sto som item 4/5 i 'Komponenter i
Scorecard'. Begge kilder hentet live denne okten og bekrefter malingen
uavhengig av 9.6: how-to-responsible-ai-scorecard enumererer summary/model
overview, data analysis, model performance, cohorts, top important factors,
fairness insights og causal insights; concept-responsible-ai-dashboard lister
Error analysis og Counterfactual what-if som *dashboard*-komponenter.

Form (b), operatorratifisert: relabel framfor fjerning. Lista renummererer
rent til 1-5 — 1,2,3,4,6,7-artefakten var tvungen kun inne i delete-only-
konvolutten, ikke for en ordinaer Edit. De to kapabilitetene beholdes i et
sitatblokk eksplisitt merket som dashboard-komponenter, sa kildebekreftet
informasjon overlever og leseren advares mot nettopp den forvekslingen som
skapte defekten. Lokator 2: Risk assessment-raden leser na 'fairness insights'.

Confidence-stempelet tolv linjer under vouchet for den falske lista og la
utenfor enhver maskinsjekk (V1/V2/V2b/V3 er strenginvarianter; check-g7-queue
tester kun ankere). Beholdt, men datert 2026-08-03 for a fore re-verifiseringen.
Dokument-koherens lest etter editen (c569bdc-laerdommen).

Nabodefekt funnet av filsveipet bokfort separat som idx-26b, ikke foldet inn:
'Quantitative analyses' attribueres til Scorecard, men er en Model Card-seksjon
(samme fil, linje 77) — kryss-attribuering mellom to standarder.

Suite 1047/1047.

[skip-docs]

scripts/kb-eval/data/g7-review-queue.json

@@ -35,13 +35,26 @@
       "id": "idx-26",
       "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
       "class": "multi-locator",
-      "status": "open",
+      "status": "resolved",
       "raised": "2026-08-03",
       "summary": "The Responsible AI Scorecard component list names Error analysis (item 4) and Counterfactual analysis (item 5). Both were measured false against first-party docs 2026-08-03: the canonical scorecard segments are summary/model overview, data analysis, model performance, cohorts, top important factors, fairness insights and causal insights; Error analysis and Counterfactual analysis are Responsible AI *dashboard* components. The delete-only reduction could only remove item 5, because line 300 asserts Error analysis as scorecard content too — so a partial fix would have left a known-false claim standing while introducing a renumbering artifact (1,2,3,4,6,7). Operator declined the partial fix 2026-08-03 and sent the whole case here. Correct repair spans both locators.",
       "evidence": "docs/r11-pilot-results.md §9.6; https://learn.microsoft.com/azure/machine-learning/how-to-responsible-ai-scorecard",
       "anchors": [
         "4. **Error analysis**: Error rates per cohort, confusion matrices",
         "| **Risk assessment** | Responsible AI Scorecard: Error analysis, fairness assessment |"
+      ],
+      "resolution": "Operator-ratified 2026-08-03, form (b) — relabel rather than remove. Both source pages were re-fetched live this session and confirm the measurement independently of §9.6: how-to-responsible-ai-scorecard enumerates summary/model overview, data analysis, model performance, cohorts, top important factors, fairness insights and causal insights; concept-responsible-ai-dashboard lists Error analysis and Counterfactual what-if among the dashboard components. Locator 1: items 4 and 5 removed from the numbered scorecard list, which renumbers cleanly to 1-5 — the 1,2,3,4,6,7 artifact was forced only inside the delete-only envelope and does not apply to an ordinary Edit. The two capabilities are retained in a blockquote explicitly marked as dashboard components rather than scorecard segments, so genuine source-confirmed information survives and the reader is warned off precisely the conflation that produced the defect. Locator 2: the Risk assessment row now reads 'fairness insights' alone. The **Confidence:** Verified stamp twelve lines below vouched for the false list and was silently outside every machine check (V1/V2/V2b/V3 are string invariants; check-g7-queue only tests anchors); it is kept but dated to 2026-08-03 to record the re-verification. Document coherence around both locators was read after the edit, per the c569bdc lesson. A neighbouring defect surfaced by the file sweep is booked separately as idx-26b rather than folded in here."
+    },
+    {
+      "id": "idx-26b",
+      "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
+      "class": "replacement",
+      "status": "open",
+      "raised": "2026-08-03",
+      "summary": "Surfaced by the post-edit file sweep for idx 26, in the same compliance-mapping table as idx 26's second locator, but outside both of its anchors — so booked separately rather than folded in (gap discipline; operator-ratified 2026-08-03). The Accuracy metrics row attributes 'Quantitative analyses' to the Responsible AI Scorecard. That is not a scorecard segment name: how-to-responsible-ai-scorecard calls the corresponding segment 'model performance'. The defect is a cross-attribution between two different standards rather than mere imprecision — 'Quantitative Analyses' is a canonical Model Card section, and this same file lists it as one at line 77. Repair is a replacement, so it is outside the delete-only envelope. Whether the right fix is to rename the segment or to drop the row is not yet decided.",
+      "evidence": "docs/r11-pilot-results.md §9.6; https://learn.microsoft.com/azure/machine-learning/how-to-responsible-ai-scorecard",
+      "anchors": [
+        "| **Accuracy metrics** | Responsible AI Scorecard: Quantitative analyses |"
       ]
     },
     {

skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md

@@ -114,10 +114,10 @@ PDF-rapport designet for å dele model- og data-innsikter mellom tekniske og ikk
 1. **Model overview**: Architecture, training data, intended use
 2. **Fairness assessment**: Performance disparities across sensitive groups (gender, ethnicity, age)
 3. **Model interpretability**: Feature importance (global/local explanations)
-4. **Error analysis**: Error rates per cohort, confusion matrices
-5. **Counterfactual analysis**: What-if scenarios (e.g., "loan approved if income +10k")
-6. **Causal inference**: Causal vs correlational relationships i features
-7. **Data quality**: Dataset statistics, missing values, outlier analysis
+4. **Causal inference**: Causal vs correlational relationships i features
+5. **Data quality**: Dataset statistics, missing values, outlier analysis
+
+> **Ikke scorecard-segmenter:** Error analysis og Counterfactual analysis er komponenter i Responsible AI *dashboard*, ikke segmenter i scorecard-PDF-en. Scorecard-en eksporterer innsikter fra dashboard-et, men de to komponentene har ingen egne segmenter i PDF-rapporten.
 
 **Customization:**
 - Target values: Akseptabel accuracy, max error rate per subgroup
@@ -126,7 +126,7 @@ PDF-rapport designet for å dele model- og data-innsikter mellom tekniske og ikk
 
 **Status:** Public preview (Azure ML) — anbefalt for production use med awareness om SLA-limitations.
 
-**Confidence:** Verified (MCP: microsoft-learn)
+**Confidence:** Verified (MCP: microsoft-learn, 2026-08-03)
 
 ---
 
@@ -297,7 +297,7 @@ Trenger du auditability for compliance?
 |----------------|----------------|
 | **Documentation av intended purpose** | Transparency Note: "Capabilities" + "Evaluating and integrating" |
 | **Description of system architecture** | Transparency Note: "The basics of [system]" |
-| **Risk assessment** | Responsible AI Scorecard: Error analysis, fairness assessment |
+| **Risk assessment** | Responsible AI Scorecard: fairness insights |
 | **Human oversight measures** | Transparency Note: "System performance" (review interventions) |
 | **Accuracy metrics** | Responsible AI Scorecard: Quantitative analyses |
 | **Data governance** | Datasheet + Azure Purview lineage tracking |