Computer Science > Machine Learning
[Submitted on 27 Nov 2022 (this version), latest version 9 Aug 2023 (v2)]
Title:Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models
View PDFAbstract:A growing ecosystem of large, open-source foundation models has reduced the labeled data and technical expertise necessary to apply machine learning to many new problems. Yet foundation models pose a clear dual-use risk, indiscriminately reducing the costs of building both harmful and beneficial machine learning systems. To mitigate this risk, we propose the task blocking paradigm, in which foundation models are trained with an additional mechanism to impede adaptation to harmful tasks while retaining good performance on desired tasks. We call the resulting models self-destructing models, inspired by mechanisms that prevent adversaries from using tools for harmful purposes. We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning, showing that it can largely prevent a BERT-based model from learning to perform gender identification without harming the model's ability to perform profession classification. We conclude with a discussion of future directions.
Submission history
From: Peter Henderson [view email][v1] Sun, 27 Nov 2022 21:43:45 UTC (385 KB)
[v2] Wed, 9 Aug 2023 00:04:38 UTC (1,231 KB)
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