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[2602.21773] Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias
Summary
This paper discusses the challenges of machine unlearning in the presence of biased data, introducing a novel framework called CUPID to enhance unlearning effectiveness while addressing unintended biases.
Why It Matters
As machine learning models increasingly influence decision-making processes, ensuring they can effectively unlearn biased data is crucial for data privacy and model reliability. This research addresses a significant gap in current methodologies by proposing a framework that improves unlearning performance, thereby contributing to more ethical AI practices.
Key Takeaways
- Machine unlearning is essential for data privacy and reliability.
- The phenomenon of 'shortcut unlearning' complicates the unlearning process in biased models.
- CUPID framework effectively partitions data based on bias sharpness to improve unlearning.
- Extensive experiments demonstrate CUPID's state-of-the-art performance in forgetting biased data.
- Addressing bias in machine learning is critical for ethical AI development.
Computer Science > Machine Learning arXiv:2602.21773 (cs) [Submitted on 25 Feb 2026] Title:Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias Authors:JuneHyoung Kwon, MiHyeon Kim, Eunju Lee, Yoonji Lee, Seunghoon Lee, YoungBin Kim View a PDF of the paper titled Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias, by JuneHyoung Kwon and 5 other authors View PDF HTML (experimental) Abstract:Machine unlearning, which enables a model to forget specific data, is crucial for ensuring data privacy and model reliability. However, its effectiveness can be severely undermined in real-world scenarios where models learn unintended biases from spurious correlations within the data. This paper investigates the unique challenges of unlearning from such biased models. We identify a novel phenomenon we term ``shortcut unlearning," where models exhibit an ``easy to learn, yet hard to forget" tendency. Specifically, models struggle to forget easily-learned, bias-aligned samples; instead of forgetting the class attribute, they unlearn the bias attribute, which can paradoxically improve accuracy on the class intended to be forgotten. To address this, we propose CUPID, a new unlearning framework inspired by the observation that samples with different biases exhibit distinct loss landscape sharpness. Our method first partitions the forget set into causal- and bias-approximated subsets based on sample sharpness, then disentangles model parameters into caus...
