When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs

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 whether points that have a negligible impact on the model’s learning need to be removed. Through a comparative analysis of influence functions across langua

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ethics.ai (13 August 2026), “When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs,” evidence record 19429, https://ethics.ai/record/19429 (originally published by Apple Machine Learning Research).

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