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AI Control

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How do we avoid harms from future AI systems that are deployed autonomously and are potentially more capable than humans? Most efforts so far have focused on alignment: getting the AI to want what we want. In this presentation, we focus on an orthogonal approach: developing protocols to deploy AIs safely even if they are misaligned. This approach is called AI control. The two main ingredients in AI control are

Control protocols – AI deployment protocols designed to be robust to models acting against us (e.g. monitoring the AI’s outputs with a less capable AI system)

Methods to evaluate control protocols

In our presentation we will 1) present a paper [1] on using AI control in a setting where a smart but misaligned LLM sometimes tries to insert backdoors into code, and 2) discuss the broader control agenda and the associated challenges. [1] Greenblatt, R., Shlegeris, B., Sachan, K., & Roger, F. (2023). AI Control: Improving safety despite intentional subversion.

This talk is part of the Machine Learning Reading Group @ CUED series.

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