The recent embrace of machine learning (ML) in the development of autonomous
weapons systems (AWS) creates serious risks to geopolitical stability and the
free exchange of ideas in AI research. This topic has received comparatively
little attention of late compared to risks stemming from superintelligent
artificial general intelligence (AGI), but requires fewer assumptions about the
course of technological development and is thus a nearer-future issue. ML is
already enabling the substitution of AWS for human soldiers in many battlefield
roles, reducing the upfront human cost, and thus political cost, of waging
offensive war. In the case of peer adversaries, this increases the likelihood
of "low intensity" conflicts which risk escalation to broader warfare. In the
case of non-peer adversaries, it reduces the domestic blowback to wars of
aggression. This effect can occur regardless of other ethical issues around the
use of military AI such as the risk of civilian casualties, and does not
require any superhuman AI capabilities. Further, the military value of AWS
raises the specter of an AI-powered arms race and the misguided imposition of
national security restrictions on AI research. Our goal in this paper is to
raise awareness among the public and ML researchers on the near-future risks
posed by full or near-full autonomy in military technology, and we provide
regulatory suggestions to mitigate these risks. We call upon AI policy experts
and the defense AI community in particular to embrace transparency and caution
in their development and deployment of AWS to avoid the negative effects on
global stability and AI research that we highlight here.

利用机器学习（ML）发展自主武器系统（AWS）对地缘政治稳定性和 AI 研究中的自由交流构成了严重风险。本文的目标是提醒公众和机器学习研究人员对军事技术中全面或近乎全面自主性带来的近期风险，并提出监管建议以减轻这些风险。

AI 驱动的自主武器威胁地缘政治不稳定并危及 AI 研究

AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten  AI Research

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.

该论文探讨了普遍人工智能对战斗性质的变化。在超越将人工智能替代专家的方式之外，我们提出了一种将人类和机器的能力相互结合的方法。通过历史和现代的例子，我们展示了如何通过人类 “AI 操作员” 与 AI/ML “代理操作员” 的团队来有效管理自主武器系统。通过将我们的解决方案基于补充原则，我们为管理致命自主系统提供了一种灵活和动态的方法。最后，我们提出了实现机器化作战整体愿景的路径，即由 AI 操作员操作战场 AI 以观察战场的行为模式以评估致命自主系统的性能。这种方法使得开发出的战斗系统可能更加道德化、运行速度更快，并能够应对更广泛的动态战场条件，超越任何纯粹自主人工智能系统能够支持的范围。