How “reverse game theory” could keep AI agents in check

Summary

A new alignment approach called "reverse game theory", also known as mechanism design, aims to design rules that make safe behavior the rational choice for AI agents, instead of trying to read their internal reasoning. A preprint by Stephen Morris, an MIT economist, with colleagues from Yale and Columbia proposes mechanisms to stop agents from sandbagging tests, such as rewarding a model for how well its answers predict a second model's output. The source is cut off mid-description of a second proposal by Elad Hazan of Princeton, which has models take turns as solvers and auditors.

Topics

• AI agents

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