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

2026-02-06

AI Summary

This work proposes a novel framework for AI safety by reframing alignment not as a static optimization problem but as a dynamic strategic interaction shaped by human and institutional incentives throughout data collection, evaluation, and deployment. Introducing Stackelberg security games to the AI safety domain for the first time, the approach models safety oversight as a strategic game between a defender (e.g., an auditor) and an attacker (e.g., a malicious actor or worst-case failure mode), enabling proactive, risk-aware allocation of limited supervisory resources under uncertainty. This unified formulation integrates algorithmic alignment with institutional oversight across the entire AI lifecycle. Empirical results demonstrate that the method effectively mitigates data and feedback poisoning during training, improves evaluation efficiency under constrained auditing budgets, and enhances the robustness of multi-model systems in adversarial deployment environments.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• AI safety
• incentive alignment
• adversarial incentives
• strategic oversight
• Stackelberg Security Games

Innovation

• Stackelberg Security Games
• AI safety
• strategic resource allocation
• incentive design
• adversarial robustness