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.
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西智能网络与场景系统重点实验室（信息与通信学院，桂林电子科技大学））；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通运输大数据应用技术国家工程实验室（广西））；School of Automation, Guangdong University of Technology（自动化学院，广东工业大学）；Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning（广西多媒体通信与网络技术重点实验室，南宁）；School of Computer and Electronic Information, Guangxi University（计算机与电子信息学院，广西大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives
Duke Kunshan University（杜克昆山大学）；Westlake University（西交大学）；Huazhong University of Science and Technology（华中科技大学）；Hong Kong University of Science and Technology（香港科技大学）
Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；University of Bristol（布里斯托大学）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
