{
  "notice": "Illustrative mapping from the AI Governance Engineer Body of Knowledge v0.5.0 (not a claim of conformity)",
  "version": "0.5.0",
  "license": "CC BY 4.0",
  "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
  "schemaVersion": 1,
  "schema": "https://aigovernanceengineer.com/api/v1/schemas/control.json",
  "self": "https://aigovernanceengineer.com/api/v1/controls/aige-ctl-deploy-009.json",
  "source": "https://aigovernanceengineer.com/controls/deployment-and-monitoring#aige-ctl-deploy-009",
  "citation": {
    "title": "AI Governance Engineering: The Thesis & Body of Knowledge",
    "authors": [
      "Jorge García Aibar"
    ],
    "parentDoi": "https://doi.org/10.5281/zenodo.22956197",
    "conceptDoi": "https://doi.org/10.5281/zenodo.22857084"
  },
  "control": {
    "id": "AIGE-CTL-DEPLOY-009",
    "profile": "deployment-and-monitoring",
    "url": "https://aigovernanceengineer.com/controls/deployment-and-monitoring#aige-ctl-deploy-009",
    "json": "https://aigovernanceengineer.com/api/v1/controls/aige-ctl-deploy-009.json",
    "title": "Fairness monitored by group in production",
    "version": "0.1",
    "status": "draft",
    "reviewerStatus": "open",
    "depth": "derived",
    "objective": "Fairness keeps being measured after go-live: selection or approval rates by group against the eval baseline, error and calibration rates by group once outcomes arrive, override, complaint and appeal rates by group, groundedness and refusal rates by topic and language for generative systems, and feedback-loop checks where outputs shape future training data; a breach opens a ticket with an owner.",
    "failureModes": [
      "A system that passed its fairness evals at go-live drifts into unfairness without any code change, and nothing measures it.",
      "The group attribute is absent at runtime and no consented sample, periodic audit or secured join replaces it, so no per-group rate exists.",
      "Reviewers override one group more often than others and the signal is never read.",
      "Outputs shape the data the next version learns from, and no feedback-loop check runs."
    ],
    "scope": "AI systems in production that make or inform decisions about people, or serve groups that can be served unequally, such as speakers of different languages. A disparity that caused harm is an incident and follows the incident controls.",
    "enforcementPoints": [
      "runtime",
      "periodic"
    ],
    "verification": [],
    "evidence": [
      {
        "artefact": "Per-group metrics in the monitoring plan, with the label delay stated, thresholds and owners, and an evidence record per check",
        "schemaId": "post-market-monitoring-plan",
        "schema": "https://aigovernanceengineer.com/schemas/post-market-monitoring-plan.v1.json",
        "layer": 4
      }
    ],
    "failureResponse": {
      "effect": "alert",
      "text": "A per-group breach opens a ticket with an owner, not a chart nobody reads; a disparity that caused harm is opened as an incident."
    },
    "layer": 4,
    "secondaryLayers": [
      5
    ],
    "patterns": [
      {
        "slug": "drift-fairness-monitor",
        "title": "Drift & Fairness Monitor",
        "url": "https://aigovernanceengineer.com/patterns/drift-fairness-monitor"
      }
    ],
    "seeds": [],
    "derivedFrom": [
      {
        "kind": "pattern",
        "ref": "drift-fairness-monitor",
        "url": "https://aigovernanceengineer.com/patterns/drift-fairness-monitor"
      },
      {
        "kind": "chapter",
        "ref": "fairness-and-explainability",
        "url": "https://aigovernanceengineer.com/bok/fairness-and-explainability"
      },
      {
        "kind": "chapter",
        "ref": "governing-deployment",
        "url": "https://aigovernanceengineer.com/bok/governing-deployment"
      }
    ],
    "mappings": {
      "obligations": [
        {
          "id": "AIGE-OBL-EUAIA-ART15-4",
          "name": "EU AI Act Art. 15(4) feedback loops in systems that continue to learn",
          "url": "https://aigovernanceengineer.com/obligations/aige-obl-euaia-art15-4"
        },
        {
          "id": "AIGE-OBL-EUAIA-ART4A",
          "name": "EU AI Act Art. 4a lawful basis for special-category data in bias detection",
          "url": "https://aigovernanceengineer.com/obligations/aige-obl-euaia-art4a"
        }
      ],
      "iso42001": [
        {
          "id": "A.6.2.6",
          "title": "AI system operation and monitoring"
        }
      ],
      "nistAiRmf": [
        {
          "id": "MEASURE 2.11",
          "title": "Fairness and bias – as identified in the MAP function – are evaluated and results are documented."
        }
      ],
      "owasp": [],
      "atlas": [],
      "aiuc1": [],
      "csaAicm": [],
      "other": []
    },
    "references": [
      {
        "n": 32,
        "title": "Fairness and explainability for practitioners",
        "text": "Fairness and explainability for practitioners (AI Governance Engineering Body of Knowledge v0.5.0, chapter 16, section \"Monitoring fairness in production\"). AI Governance Engineer (Jorge García Aibar). 2026-09.",
        "url": "https://aigovernanceengineer.com/bok/fairness-and-explainability#monitoring-fairness-in-production",
        "verified": "primary"
      },
      {
        "n": 33,
        "title": "Governing deployment and use",
        "text": "Governing deployment and use (AI Governance Engineering Body of Knowledge v0.5.0, chapter 15, section \"Fairness and quality in production\"). AI Governance Engineer (Jorge García Aibar). 2026-09.",
        "url": "https://aigovernanceengineer.com/bok/governing-deployment#fairness-and-quality-in-production",
        "verified": "primary"
      },
      {
        "n": 27,
        "title": "Pattern: Drift & Fairness Monitor",
        "text": "Pattern: Drift & Fairness Monitor (AI Governance Engineering Body of Knowledge v0.5.0, pattern catalogue (chapter 05)). AI Governance Engineer (Jorge García Aibar). 2026-09.",
        "url": "https://aigovernanceengineer.com/patterns/drift-fairness-monitor",
        "verified": "primary"
      },
      {
        "n": 34,
        "title": "Regulation (EU) 2024/1689 (AI Act), consolidated text of 2026-07-27, Art. 15",
        "text": "Regulation (EU) 2024/1689 (AI Act), consolidated text of 2026-07-27, Art. 15 (15(4): systems that continue to learn reduce the risk of biased outputs feeding future input, \"feedback loops\"). Publications Office of the EU (EUR-Lex). 2026-07-27.",
        "url": "https://eur-lex.europa.eu/eli/reg/2024/1689/2026-07-27/eng#art_15",
        "verified": "primary"
      },
      {
        "n": 35,
        "title": "Regulation (EU) 2024/1689 (AI Act), consolidated text of 2026-07-27, Arts. 4a and 10",
        "text": "Regulation (EU) 2024/1689 (AI Act), consolidated text of 2026-07-27, Arts. 4a and 10 (as amended by Reg. (EU) 2026/1744: Art. 10(5) deleted; Art. 4a inserted for special-category data in bias detection and correction). Publications Office of the EU (EUR-Lex). 2026-07-27.",
        "url": "https://eur-lex.europa.eu/eli/reg/2024/1689/2026-07-27/eng#art_4a",
        "verified": "primary"
      },
      {
        "n": 5,
        "title": "ISO/IEC 42001:2023, AI management systems, Annex A",
        "text": "ISO/IEC 42001:2023, AI management systems, Annex A (reference control objectives and controls A.2 to A.10, cited by id and short title). ISO/IEC. 2023.",
        "url": "https://www.iso.org/standard/81230.html",
        "verified": "secondary"
      },
      {
        "n": 6,
        "title": "Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1",
        "text": "Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (subcategories cited by id: GOVERN 1.6, 1.7, 2.2, 6.1; MAP 1.1, 3.5; MEASURE 2.3, 2.4, 2.11, 3.1; MANAGE 1.1, 2.4, 3.1, 4.1, 4.3). NIST. 2023-01-26.",
        "url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf",
        "verified": "primary"
      }
    ],
    "implementationNotes": [
      "Where the group attribute is not held at runtime, choose between a consented sample or panel, periodic audits under the Art. 4a conditions, or outcome-free rates with the attribute joined in a secured environment.",
      "The contest channel is a sensor: complaints, appeals and explanation requests by group, with their outcomes, feed the same threshold and issue path as every other signal.",
      "Where law requires a periodic bias audit, as New York City's Local Law 144 does for automated employment decision tools, the production telemetry is what makes the audit cheap."
    ],
    "openQuestions": [
      "Verification procedure to be specified: the source material states what the control produces, not how a third party checks it; requires technical review.",
      "Per-group metrics on small groups are noisy; the source material names the problem but sets no minimum group size or window."
    ],
    "observation": null,
    "observationSchema": "https://aigovernanceengineer.com/schemas/control-observation.v1.json",
    "examples": []
  }
}