Incentive-Aware AI Safety via Strategic Resource Allocation: A Stackelberg Security Games Perspective

AI safety requires strategic oversight of institutions, not just model alignment: The paper argues that existing safety approaches focus too narrowly on tuning AI models themselves, while overlooking the human and institutional incentives that shape how AI systems are actually built and deployed. This means safety depends on designing systems that account for how people might be motivated to cut corners or act maliciously throughout the AI development process.

Limited oversight resources should be strategically allocated like security defense: Using game theory models from security research, the framework suggests that auditors and evaluators should concentrate their limited time and resources on the highest-risk areas rather than spreading efforts evenly. The paper illustrates this across three critical stages: catching poisoned training data, reviewing models before deployment with constrained reviewer capacity, and protecting systems once deployed in adversarial environments.

Adversarial thinking should be built into AI safety from the start: Rather than assuming good faith from all participants, the framework treats AI development as a strategic interaction where some actors may intentionally try to compromise safety (through data poisoning, biased feedback, or other manipulation). By anticipating these adversarial scenarios during design, oversight systems can be made more resilient and harder to circumvent.

Game-theoretic deterrence can make AI oversight more proactive: The paper suggests that well-designed incentive structures—knowing where auditing will focus, understanding consequences for misconduct—can deter bad actors before problems occur, rather than only catching failures after they happen. This shifts oversight from reactive to preventive.