{
  "id": 11577,
  "url": "https://machinelearning.apple.com/research/unlearning-free-low-influence",
  "title": "When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs",
  "summary": "As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important. While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking: do points that have a negligible impact on the model’s learning need to be removed? Through a comparative analysis of influence functions across language and",
  "authors": null,
  "category": "org",
  "topics": "privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-17T00:00:00.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "x-apple-machine-learning-research",
  "source_name": "Apple Machine Learning Research",
  "source_homepage": "https://machinelearning.apple.com",
  "ethics_ai_record_url": "https://ethics.ai/record/11577",
  "original_url": "https://machinelearning.apple.com/research/unlearning-free-low-influence",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}