Mechanism design meets AI alignment: new framework targets sandbagging and alignment faking
DavideCrapis · x · 2026-09-03
Andrew Koh and collaborators release a largely conceptual framework applying mechanism design to AI alignment and control. The argument: as models grow more capable, directly shaping their preferences gets harder, so we should design rules (evals, permissions, rewards) that yield good outcomes even when preferences are unknown or bad. The paper offers stylized applications to failure modes like sandbagging and alignment faking, safety practices like scalable oversight and peer prediction, and a way to reason about the value of alignment, interpretability, capability and control. Forwarder Mallesh Pai calls it a forming research line worth pursuing.
Related event: Mechanism Design Framework Proposed for AI Alignment and Control(9 posts)→
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