When deploying AI agents whose true capabilities and preferences are hidden, you can use mechanism design principles—like nested monotonicity conditions and higher-order belief elicitation—to create incentives that force honest revelation and obedient behavior.
This paper develops a framework for designing mechanisms that incentivize AI agents to be honest about their preferences and obedient in their actions, even when their true capabilities and alignment are unknown. The authors show how to detect deception (like sandbagging), balance alignment with interpretability, and use peer scoring and competition to ensure agents act as intended.