AI Governance Engineer
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Output suppression

By Updated

A filter around a model that stops it producing a person's data: the fast first answer to an erasure or objection request when the data sits in the weights and retraining is disproportionate. The CNIL accepts filters shown to be effective and robust and prefers general rules to a list of names 1. The data stays in the model.

Developed in
ch. 19, Suppression, retraining and unlearning
ch. 05, Pattern: Rights Requests Against Models
Chapters
ch. 05, Patterns · ch. 19, Privacy & AI
Contrast with
Machine unlearning
Source
1 numbered reference, listed below

Where it is used

One chapter of the Body of Knowledge uses the term. Each link opens the first section that does.

Patterns that use this term

One pattern page uses the term.

  • Fulfilment record

    The per-request record of how a data-subject request was honoured wherever the person's data sits, from source systems, snapshots, retrieval indexes, logs and…

Sources

  1. [1] "Ensuring and facilitating the exercise of data subjects' rights" (AI how-to sheet; retraining; output filters accepted if shown sufficiently effective and robust, based on general rules rather than lists of people). CNIL. 2026-01-05. https://www.cnil.fr/en/respect-and-facilitate-exercise-data-subjects-rights (verified: primary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Output suppression. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/output-suppression. CC BY 4.0

BibTeX

@misc{aige2026outputsuppression,
  author  = {Jorge García Aibar},
  title   = {{Output suppression}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/output-suppression}
}