{
  "id": 5754,
  "url": "https://arxiv.org/abs/2604.16536v1",
  "title": "Towards Reliable Testing of Machine Unlearning",
  "summary": "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",
  "authors": "Anna Mazhar, Sainyam Galhotra",
  "category": "research",
  "topics": "regulation,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-16T21:01:28.000Z",
  "fetched_at": "2026-07-14T16:31:57.535Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5754",
  "original_url": "https://arxiv.org/abs/2604.16536v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}