Open Problems in Machine Unlearning for AI Safety
Journal:
arXiv
Published Date:
Jan 9, 2025
Abstract
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.
