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

TL;DR

This paper introduces a game-theoretic framework based on Stackelberg Security Games to improve AI safety by strategically allocating oversight resources and accounting for adversarial incentives throughout the AI lifecycle.

Contribution

It applies Stackelberg Security Games to model AI safety oversight, integrating incentive design and adversarial considerations into a unified strategic framework.

Findings

Framework informs training-time auditing against poisoning attacks

Guides resource-constrained pre-deployment evaluation

Supports robust multi-model deployment in adversarial settings

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.