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Open Problems in Machine Unlearning for AI Safety
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As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and healthcare, ensuring their safety and alignment with human values is paramount. Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks, which has been the primary focus of existing research. More recently, its potential application to AI safety has gained attention. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety, particularly in managing dual-use knowledge in sensitive domains like cybersecurity and chemical, biological, radiological, and nuclear (CBRN) safety. In these contexts, information can be both beneficial and harmful, and models may combine seemingly harmless information for harmful purposes -- unlearning this information could strongly affect beneficial uses. We provide an overview of inherent constraints and open problems, including the broader side effects of unlearning dangerous knowledge, as well as previously unexplored tensions between unlearning and existing safety mechanisms. Finally, we investigate challenges related to evaluation, robustness, and the preservation of safety features during unlearning. By mapping these limitations and open challenges, we aim to guide future research toward realistic applications of unlearning within a broader AI safety framework, acknowledging its limitations and highlighting areas where alternative approaches may be required.
Forward citations
Cited by 9 Pith papers
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RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories
RippleBench automatically generates questions at increasing semantic distance from unlearned topics and shows all eight tested unlearning methods degrade accuracy that recovers only slowly with distance.
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Minimalist Concept Erasure in Generative Models
A final-output-only loss with learned neuron masks erases concepts from flow-based image generators more robustly than per-step fine-tuning methods.
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Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs
Unlearning in LLMs leaves detectable 'fingerprints' that let a simple classifier distinguish an unlearned model from its original, even on unrelated prompts.
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LLM Unlearning Should Be Form-Independent
Existing LLM unlearning is form-dependent; the new ORT benchmark measures this, and the training-free ROCR edit reduces it by redirecting concept representations.
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Existing Large Language Model Unlearning Evaluations Are Inconclusive
Existing LLM unlearning evaluations are inconclusive: they can inject new information, depend heavily on task format, and rely on spurious correlations.
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Model Unlearning via Sparse Autoencoder Subspace Guided Projections
SSPU uses SAE-derived subspaces to guide weight updates, lowering WMDP-Cyber accuracy by 3.22% more than RMU while largely preserving MMLU, TruthfulQA, and GSM8K performance.
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The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It
LLM safety research at ACL venues from 2020 to 2024 is predominantly English-only, and the language gap is growing over time.
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A Survey on Generative Model Unlearning: Fundamentals, Taxonomy, Evaluation, and Future Direction
A survey and framework that categorizes generative model unlearning by point-wise versus concept-wise objectives, parameter-based versus non-parametric methods, and completeness/utility/efficiency evaluation.
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UCD: Unlearning in LLMs via Contrastive Decoding
UCD steers an LLM away from forget-set content at inference time by mixing in the difference between forget-tuned and retain-tuned small models.
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