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 lo
Record details
Published: 16 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Healthcare
Retrieved: 14 July 2026
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ethics.ai (16 April 2026), “Towards Reliable Testing of Machine Unlearning,” evidence record 5754, https://ethics.ai/record/5754 (originally published by arXiv).
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