Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence
This work addresses the societal safety problem posed by AI's dual-use nature, providing a conceptual foundation for researchers and policymakers, though it is incremental as it builds on existing work.
The paper tackles the challenge of understanding how AI can both cause harm and enhance protection, proposing a taxonomy to map key factors influencing offense-defense dynamics in AI systems.
The rapid advancement of artificial intelligence (AI) technologies presents profound challenges to societal safety. As AI systems become more capable, accessible, and integrated into critical services, the dual nature of their potential is increasingly clear. While AI can enhance defensive capabilities in areas like threat detection, risk assessment, and automated security operations, it also presents avenues for malicious exploitation and large-scale societal harm, for example through automated influence operations and cyber attacks. Understanding the dynamics that shape AI's capacity to both cause harm and enhance protective measures is essential for informed decision-making regarding the deployment, use, and integration of advanced AI systems. This paper builds on recent work on offense-defense dynamics within the realm of AI, proposing a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society. By establishing a shared terminology and conceptual foundation for analyzing these interactions, this work seeks to facilitate further research and discourse in this critical area.