Deep neural networks are at the center of rapid progress in AI, with applications to computer vision, natural language processing, speech recognition and others. While this progress offers many exciting opportunities, it also introduces new challenges, as we researchers bear the responsibility to understand and mitigate the potential risks associated with it. Notably, a key ingredient of recent advances is the role of massive datasets combined with ever larger models. This aspect has implications for privacy, as large models tend to memorize details of their training set. Concretely, the field faces a significant challenge meeting recent privacy regulations, such as EU’s General Data Protection Regulation (Mantelero, 2013) or Canada’s Personal Information Protection and Electronic Documents Act, which stipulate that individuals have the “right to be forgotten”.
The introduction of this legal notion has spurred the development of formal, mathematical notions of “deleting” or “obliterating” one’s data, all studied under the auspices of “machine unlearning”. Informally, unlearning refers to removing the influence of a subset of the training set from the weights of a trained model. The development of novel formal models, their theoretical limitations, and efficient and scalable algorithms is a rich and growing subfield; see for example recent surveys by Zhang et al. (2023), Nguyen et al. (2022), Jiang et al. (2022).
Machine unlearning is a powerful tool that has the potential to address a number of important problems. As research in this area continues, we can expect to see new methods that are more efficient, effective, and ethical. We are thrilled to have the opportunity via this competition to spark interest in this field, and we are looking forward to sharing our insights and findings with the community.