Documentation Index
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The Model Governance API provides controls for responsible AI deployment: approval workflows before production promotion, automated drift detection, and fairness parity checks across customer segments.
GET /api/v1/model-governance
Retrieve governance status for a model including approval history and drift check results.
Query Parameters
| Parameter | Type | Required | Description |
|---|
modelId | string | Yes | Algorithm model ID |
Response
{
"modelId": "clx...",
"approvals": [
{
"id": "clx...",
"modelId": "clx...",
"version": 3,
"requestedBy": "data-scientist@example.com",
"status": "approved",
"reviewedBy": "admin@example.com",
"reason": "AUC improved from 0.82 to 0.87",
"createdAt": "2026-03-18T10:00:00.000Z"
}
],
"driftChecks": [
{
"id": "clx...",
"modelId": "clx...",
"version": 3,
"driftDetected": false,
"psiScore": 0.04,
"aucDelta": -0.01,
"checkedAt": "2026-03-18T12:00:00.000Z"
}
]
}
POST /api/v1/model-governance
Perform governance actions. Editor or Admin (review requires Admin).
Actions
{
"action": "request_approval",
"modelId": "clx...",
"version": 3,
"metrics": { "auc": 0.87, "precision": 0.82, "recall": 0.79 }
}
{ "approvalId": "clx...", "status": "pending" }
{
"action": "review",
"approvalId": "clx...",
"decision": "approved",
"reason": "AUC meets threshold, no drift detected"
}
{ "approvalId": "clx...", "status": "approved" }
drift_check — Run a drift detection check
Compares baseline vs. current score distributions using three methods:
- PSI (Population Stability Index): measures shift between score distributions (< 0.1 OK, 0.1–0.25 monitor, > 0.25 retrain)
- KS (Kolmogorov-Smirnov): max CDF difference between distributions
- AUC decay: drop in AUC from baseline
Each check is persisted to the ModelDriftCheck table.
{
"action": "drift_check",
"modelId": "clx...",
"version": 3,
"baselineScores": [0.1, 0.3, 0.5, 0.7, 0.9],
"currentScores": [0.15, 0.35, 0.45, 0.65, 0.85],
"baselineAuc": 0.85,
"currentAuc": 0.82,
"thresholds": { "psi": 0.25, "ks": 0.1, "aucDecay": 0.05 }
}
{
"psi": { "score": 0.03, "drifted": false },
"ks": { "score": 0.05, "drifted": false },
"aucDecay": { "score": 0.03, "drifted": false },
"anyDrift": false
}
| Field | Type | Description |
|---|
psi.score | number | Population Stability Index (0 = identical distributions) |
psi.drifted | boolean | true if PSI exceeds threshold (default 0.25) |
ks.score | number | Kolmogorov-Smirnov statistic (0–1) |
ks.drifted | boolean | true if KS exceeds threshold (default 0.1) |
aucDecay.score | number | max(0, baselineAuc - currentAuc) |
aucDecay.drifted | boolean | true if AUC dropped more than threshold (default 0.05) |
anyDrift | boolean | true if any of the three checks flagged drift |
parity_check — Check fairness across customer segments
Compares mean scores across segments and flags those deviating more than maxDeviation from the overall mean.
{
"action": "parity_check",
"segmentScores": {
"young_adults": [0.8, 0.7, 0.9, 0.85],
"seniors": [0.3, 0.2, 0.4, 0.25],
"middle_aged": [0.5, 0.6, 0.55, 0.5]
},
"maxDeviation": 0.15
}
{
"results": [
{ "segment": "young_adults", "sampleSize": 4, "meanScore": 0.813, "stdDev": 0.075 },
{ "segment": "seniors", "sampleSize": 4, "meanScore": 0.288, "stdDev": 0.073 },
{ "segment": "middle_aged", "sampleSize": 4, "meanScore": 0.538, "stdDev": 0.041 }
],
"disparityDetected": true,
"flaggedSegments": ["young_adults", "seniors"]
}
| Field | Type | Description |
|---|
results | array | Per-segment statistics (sampleSize, meanScore, stdDev) |
disparityDetected | boolean | true if any segment deviates > maxDeviation from overall mean |
flaggedSegments | string[] | Segments exceeding the deviation threshold |
