War Elephants: Rethinking Combat AI and Human Oversight

TL;DR

The paper argues that battlefield AI will pervade operations, but reliability and ethics require human oversight rather than full automation. It proposes a complementation framework where specialized AI Operators and AI Proxy Operators monitor, adapt, and sometimes override battlefield AI to achieve machine-speed yet human-aligned combat. Drawing lessons from the history of war elephants and centaur systems, it outlines switchable-model architectures, advanced visualization tools, and a two-tier operator model (mechanical mahouts) to enhance resilience and adaptability. This approach aims to deliver LAWS that are faster and more capable while preserving accountability and adherence to IHL, with practical pathways for implementation in exercises and real deployments.

Abstract

This paper explores the changes that pervasive AI is having on the nature of combat. We look beyond the substitution of AI for experts to an approach where complementary human and machine abilities are blended. Using historical and modern examples, we show how autonomous weapons systems can be effectively managed by teams of human "AI Operators" combined with AI/ML "Proxy Operators." By basing our approach on the principles of complementation, we provide for a flexible and dynamic approach to managing lethal autonomous systems. We conclude by presenting a path to achieving an integrated vision of machine-speed combat where the battlefield AI is operated by AI Operators that watch for patterns of behavior within battlefield to assess the performance of lethal autonomous systems. This approach enables the development of combat systems that are likely to be more ethical, operate at machine speed, and are capable of responding to a broader range of dynamic battlefield conditions than any purely autonomous AI system could support.

Figures (3)

• Figure 1: Brittle Combat AI
• Figure 2: Resilient Combat AI
• Figure 3: Interfaces for Model Behavior