On Thursday, June 29, 2023, two research scientists from Google posted an announcement on the Google Research Blog about the first Machine Unlearning Challenge. I was thrilled to read the opening sentence of the second paragraph:
Fully erasing the influence of the data requested to be deleted is challenging since, aside from simply deleting it from databases where it’s stored, it also requires erasing the influence of that data on other artifacts such as trained machine learning models.
I was also encouraged further when the authors pointed out the value of Membership Inference Attacks (MIAs) as tools being employed in identifying the source data used in training datasets.
This is one of the few active projects I’ve encountered that seeks to address the need to renovate LLMs by finding and removing unwanted sources that were incorporated in the model’s training data. I’m keeping a close watch on this one.
Announcing the first Machine Unlearning Challenge
Thursday, June 29, 2023
Posted by Fabian Pedregosa and Eleni Triantafillou, Research Scientists, Google
Deep learning has recently driven tremendous progress in a wide array of applications, ranging from realistic image generation and impressive retrieval systems to language models that can hold human-like conversations. While this progress is very exciting, the widespread use of deep neural network models requires caution: as guided by Google’s AI Principles, we seek to develop AI technologies responsibly by understanding and mitigating potential risks, such as the propagation and amplification of unfair biases and protecting user privacy.
Fully erasing the influence of the data requested to be deleted is challenging since, aside from simply deleting it from databases where it’s stored, it also requires erasing the influence of that data on other artifacts such as trained machine learning models. Moreover, recent research [1, 2] has shown that in some cases it may be possible to infer with high accuracy whether an example was used to train a machine learning model using membership inference attacks (MIAs). This can raise privacy concerns, as it implies that even if an individual’s data is deleted from a database, it may still be possible to infer whether that individual’s data was used to train a model.
Given the above, machine unlearning is an emergent subfield of machine learning that aims to remove the influence of a specific subset of training examples — the “forget set” — from a trained model. Furthermore, an ideal unlearning algorithm would remove the influence of certain examples while maintaining other beneficial properties, such as the accuracy on the rest of the train set and generalization to held-out examples. A straightforward way to produce this unlearned model is to retrain the model on an adjusted training set that excludes the samples from the forget set. However, this is not always a viable option, as retraining deep models can be computationally expensive. An ideal unlearning algorithm would instead use the already-trained model as a starting point and efficiently make adjustments to remove the influence of the requested data.
Today we’re thrilled to announce that we’ve teamed up with a broad group of academic and industrial researchers to organize the first Machine Unlearning Challenge. The competition considers a realistic scenario in which after training, a certain subset of the training images must be forgotten to protect the privacy or rights of the individuals concerned. The competition will be hosted on Kaggle, and submissions will be automatically scored in terms of both forgetting quality and model utility. We hope that this competition will help advance the state of the art in machine unlearning and encourage the development of efficient, effective and ethical unlearning algorithms.
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