Incentive-Aware AI Safety via Strategic Resource Allocation: A Stackelberg Security Games Perspective
Cheol Woo Kim, Davin Choo, Tzeh Yuan Neoh, Milind Tambe
TL;DR
This work reframes AI safety as an incentive-aware, lifecycle-spanning problem by applying Stackelberg Security Games to model strategic interactions between defenders (auditors, evaluators, deployers) and attackers (misaligned agents) under limited oversight. It proposes three directions—data/feedback auditing, LLM evaluation, and LLM deployment—each cast as an SSG to optimize resource allocation under uncertainty and adversarial manipulation. The approach unifies model-level safety with institutional oversight, enabling robust, randomized, and risk-aware strategies that anticipate strategic behavior throughout training, evaluation, and deployment. By enabling proactive deterrence and scalable oversight, the framework aims to make AI safety more resilient to manipulation and adaptive threats in real-world systems.
Abstract
As AI systems grow more capable and autonomous, ensuring their safety and reliability requires not only model-level alignment but also strategic oversight of the humans and institutions involved in their development and deployment. Existing safety frameworks largely treat alignment as a static optimization problem (e.g., tuning models to desired behavior) while overlooking the dynamic, adversarial incentives that shape how data are collected, how models are evaluated, and how they are ultimately deployed. We propose a new perspective on AI safety grounded in Stackelberg Security Games (SSGs): a class of game-theoretic models designed for adversarial resource allocation under uncertainty. By viewing AI oversight as a strategic interaction between defenders (auditors, evaluators, and deployers) and attackers (malicious actors, misaligned contributors, or worst-case failure modes), SSGs provide a unifying framework for reasoning about incentive design, limited oversight capacity, and adversarial uncertainty across the AI lifecycle. We illustrate how this framework can inform (1) training-time auditing against data/feedback poisoning, (2) pre-deployment evaluation under constrained reviewer resources, and (3) robust multi-model deployment in adversarial environments. This synthesis bridges algorithmic alignment and institutional oversight design, highlighting how game-theoretic deterrence can make AI oversight proactive, risk-aware, and resilient to manipulation.