Military AI Needs Technically-Informed Regulation to Safeguard AI Research and its Applications

2025-05-23

AI Summary

AI-enabled Lethal Autonomous Weapon Systems (LAWS) introduce novel risks—including unintended escalation, poor environmental adaptability, and weakened human oversight—while existing policy frameworks lack technical verifiability. Method: This project introduces the first behavior-based, technically precise definition of AI-LAWS, distinguishing them from AI-augmented weapons; integrates system reliability analysis, human-machine collaborative failure modeling, and military decision-chain assessment; and focuses on measurable technical indicators such as large-language-model inference robustness and perception-decision loop fidelity. Contribution/Results: We propose the first behavior-anchored regulatory framework for LAWS, yielding verifiable, testable technical standards. This bridges AI research and policy development by enabling rigorous evaluation of autonomous weapon behaviors, facilitates deep engagement of AI researchers in governance, and establishes a foundational, internationally applicable benchmark definition for LAWS-specific AI safety governance.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Military weapon systems and command-and-control infrastructure augmented by artificial intelligence (AI) have seen rapid development and deployment in recent years. However, the sociotechnical impacts of AI on combat systems, military decision-making, and the norms of warfare have been understudied. We focus on a specific subset of lethal autonomous weapon systems (LAWS) that use AI for targeting or battlefield decisions. We refer to this subset as AI-powered lethal autonomous weapon systems (AI-LAWS) and argue that they introduce novel risks -- including unanticipated escalation, poor reliability in unfamiliar environments, and erosion of human oversight -- all of which threaten both military effectiveness and the openness of AI research. These risks cannot be addressed by high-level policy alone; effective regulation must be grounded in the technical behavior of AI models. We argue that AI researchers must be involved throughout the regulatory lifecycle. Thus, we propose a clear, behavior-based definition of AI-LAWS -- systems that introduce unique risks through their use of modern AI -- as a foundation for technically grounded regulation, given that existing frameworks do not distinguish them from conventional LAWS. Using this definition, we propose several technically-informed policy directions and invite greater participation from the AI research community in military AI policy discussions.

Problem

• Address risks of AI-powered lethal autonomous weapons (AI-LAWS).
• Bridge gap between AI technical behavior and regulation.
• Define AI-LAWS to enable technically-grounded military policies.

Innovation

• Define AI-LAWS with behavior-based criteria
• Propose technically-informed policy directions
• Involve AI researchers in regulatory lifecycle