EU AI Act Art. 15(4): feedback loops in systems that continue to learn

Systems that continue to learn after placing on the market are built to eliminate or reduce the risk of biased outputs feeding future inputs (feedback loops), with mitigation measures

By Jorge García Aibar Reviewed 2026-09-24

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-EUAIA-ART15-4. Drawn from chapter 08.

Text alternative

Clause: EU AI Act, Art. 15.
Duty holder: Provider.
Applies from: 2027-12-02, Deferred.
Artefact: Feedback-loop fairness monitor.
Layers: Layer 03, Layer 04.
Evidence record: Eval result, +4 more.
Record schemas: Eval result , Control observation , Test plan , Test report , Design record .
No crosswalk topic files this clause yet.

Id

AIGE-OBL-EUAIA-ART15-4

Instrument

EU AI Act (post-Omnibus) law

Compared side by side

ISO 42001 vs EU AI Act · NIST AI RMF vs EU AI Act

Clause

Art. 15(4)

Duty holder

Provider

Authority

National MSA

Applies from

2027-12-02 Deferred · Annex III

Later dates

2028-08-02 Applies to Annex I embedded (product safety-component) systems
2030-08-02 Deadline for legacy high-risk systems intended for use by public authorities (Art. 111(2))

System class

High-risk (Annex III) · High-risk (Annex I)

The artefact that evidences it

Feedback-loop fairness monitor; retraining-data bias check; agent memory write gate with provenance and rollback to a known-good snapshot.

Patterns that build it

Eval Gate in CI (layer 3)
Adversarial Red-Team Suite (layer 3)
Runtime Guardrail (layer 4)
Kill Switch / Circuit Breaker (layer 4)
Agent Identity & Scoped Credentials (layer 4)
AI Threat Model (layer 1 and 3)
Fairness Eval Suite (layer 3)
Model Artefact Integrity (layer 2 and 4)
Claims Substantiation Gate (layer 5 and 3)
Drift & Fairness Monitor (layer 4 and 5)

Open controls that evidence it

Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.

AIGE-CTL-DEPLOY-009 Fairness monitored by group in production (Deployment and monitoring profile)

Source

Chapter 08, section EU AI Act, post-Omnibus, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-euaia-art15-4.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). EU AI Act Art. 15(4): feedback loops in systems that continue to learn (AIGE-OBL-EUAIA-ART15-4). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-euaia-art15-4. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{EU AI Act Art. 15(4): feedback loops in systems that continue to learn (AIGE-OBL-EUAIA-ART15-4)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-euaia-art15-4},
  note         = {Version 0.5.0}
}