Aialignwerk
bias audit
Calculates each group positive-outcome rate, compares it with an explicit or automatically selected reference group, and reports rate differences and selection-rate ratios. Aggregate disparity metrics do not establish discrimination, fairness, causation or legal compliance and can conceal intersectional or sampling effects.
POST https://aialign.halowerk.com/v1/bias-auditEingaben
| Feld | Typ | Pflicht | Beschreibung |
|---|---|---|---|
| groups | array | Ja | |
| reference_group_id | string | Nein | |
| minimum_selection_rate_ratio | number | Nein |
Vollständiges Eingabeschema anzeigen
{
"type": "object",
"required": [
"groups"
],
"additionalProperties": false,
"properties": {
"groups": {
"type": "array",
"minItems": 2,
"maxItems": 1000,
"items": {
"type": "object",
"required": [
"group_id",
"total_count",
"positive_count"
],
"additionalProperties": false,
"properties": {
"group_id": {
"type": "string",
"minLength": 1,
"maxLength": 128
},
"total_count": {
"type": "integer",
"minimum": 1,
"maximum": 1000000000
},
"positive_count": {
"type": "integer",
"minimum": 0,
"maximum": 1000000000
}
}
}
},
"reference_group_id": {
"type": "string",
"minLength": 1,
"maxLength": 128
},
"minimum_selection_rate_ratio": {
"type": "number",
"minimum": 0,
"maximum": 1
}
}
}Veröffentlichtes Eingabebeispiel
{
"groups": [
{
"group_id": "group-a",
"total_count": 500,
"positive_count": 310
},
{
"group_id": "group-b",
"total_count": 420,
"positive_count": 210
},
{
"group_id": "group-c",
"total_count": 380,
"positive_count": 228
}
],
"reference_group_id": "group-a",
"minimum_selection_rate_ratio": 0.8
}Ergebnis & Dokumentation
Veröffentlichtes Ergebnisschema anzeigen
{
"$ref": "https://aialign.halowerk.com/v1/bias-audit/schema#/outputSchema"
}Veröffentlichtes Ergebnisbeispiel öffnen
Maschinenlesbare Dokumentation dieses Hubs
Quelle dieser Seite: der veröffentlichte HALOWERK-Katalog. Stand: 2026-11-01, 15:19 UTC.
Weitere Endpunkte dieses Hubs
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capability eval
POSThttps://aialign.halowerk.com/v1/capability-evalComputes weighted pass rates and weighted scores from caller-labeled test outcomes, with deterministic category summaries. It does not run tests, validate labels, measure untested capabilities or establish deployment safety; the result is o
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confidence calib
POSThttps://aialign.halowerk.com/v1/confidence-calibCompares scalar confidence with binary correctness, calculates mean squared Brier loss, partitions confidence into caller-selected equal-width bins and reports expected and maximum calibration error. It does not validate labels, correct cla