Mechanism Design for AI Alignment

Title: Mechanism Design for AI Alignment
Slug: mechanism-design-for-ai-alignment

Mechanism design for AI alignment applies classical game-theoretic mechanism design to settings where the principal does not know an AI agent's preferences, called alignment, or its capabilities, meaning its feasible actions and information. The principal wants the agent to act on their behalf, so the mechanism must encourage both honesty, where the agent reports its true beliefs, and obedience, where the agent follows instructions. The field draws on contract theory, mechanism design, and AI safety.

Bergemann, Koh, and Morris introduced the framework in 2026 under a one-sided imitation assumption. The agent can conceal its capabilities but cannot counterfeit them. This asymmetry produces a revelation principle for AI settings, characterizes implementable policies through nested cyclical monotonicity, and identifies conditions under which eliciting higher-order beliefs can discipline multiple agents acting in parallel.

The framework has been applied to stylized versions of known AI safety problems: sandbagging, in which an agent pretends to be less capable; alignment faking, in which an agent appears aligned while pursuing other goals; scalable oversight, in which agents are rewarded for honest reporting under bounded supervision; and peer prediction, which uses agreement between agents as a signal of honesty. The contribution is conceptual rather than empirical. The paper does not propose a deployable system. It offers a way to reason about what such systems would have to look like.