Towards Reliable Testing of Machine Unlearning
Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As regulations and governance frameworks increasingly require deleting sensitive data from deployed models, machine unlearning is emerging as a practical alternative to full retraining. However, unlearning introduces a software quality-assurance challenge: under realistic deployment constraints and imperfect oracles, how can we test that a model no longer relies on targeted information? This paper frames unlearning testing as a first-class software engineering problem. We argue that practical unlearning tests must provide (i) thorough coverage ...
| Authors | Anna Mazhar, Sainyam Galhotra |
|---|---|
| Primary category | cs.SE |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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