---
title: "Machine unlearning"
description: "Techniques that remove a training record's influence from a model without full retraining."
canonical: https://aigovernanceengineer.com/glossary/machine-unlearning
author: "Jorge García Aibar"
license: "CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)"
doi: https://doi.org/10.5281/zenodo.22956197
version: "0.5.0"
updated: 2026-09-25
---

# Machine unlearning

Techniques that remove a training record's influence from a model without full retraining. Exact methods retrain an affected shard [1]; approximate methods adjust weights and are hard to verify, so an unlearning claim is tested with membership-inference or extraction evals.

- Developed in: [ch. 19, Suppression, retraining and unlearning](https://aigovernanceengineer.com/bok/privacy-and-ai#suppression-retraining-and-unlearning); [ch. 05, Pattern: Rights Requests Against Models](https://aigovernanceengineer.com/patterns/rights-requests-against-models)
- Chapters: [ch. 05, Patterns](https://aigovernanceengineer.com/bok/patterns) · [ch. 19, Privacy & AI](https://aigovernanceengineer.com/bok/privacy-and-ai)
- Contrast with: [Output suppression](https://aigovernanceengineer.com/glossary/output-suppression)
- In the glossary chapter: https://aigovernanceengineer.com/bok/glossary#t-machine-unlearning

## Sources

[1] "Machine Unlearning" (Bourtoule et al.; SISA training; arXiv 1912.03817). arXiv. 2019-12-09. https://arxiv.org/abs/1912.03817 (verified: primary)