Abstract
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.
Abstract (translated)
近年来，机器学习（ML）在自主武器系统（AWS）的发展中得到了广泛应用，这给地缘政治稳定和人工智能研究的自由交流带来了严重风险。与超级智能人工智能（AGI）带来的风险相比，这个话题最近受到了相对较少的关注，但它离我们更近，是一个更接近未来的问题。ML 已经在许多战场角色中用 AWS 替换了人类士兵，降低了战争开端的 human cost，从而降低了政治成本。在平等对手的情况下，这增加了“低强度”冲突升级到更广泛战争的概率。在对等对手的情况下，它减少了国内反弹，降低了侵略战争造成的国内影响。这种效果可以在不影响其他涉及军事人工智能使用的伦理问题的前提下发生，也不需要超人类 AI 能力。此外，AWS 在军事上的价值加剧了 AI 驱动的军备竞赛和错误地限制国家安全研究的的国家安全限制。我们在论文中的目标是提醒公众和 ML 研究人员，在军事技术上实现完全或近完全自主可能带来的近未来风险，并向减轻这些风险提供监管建议。我们呼吁 AI 政策专家和国防 AI 社区在开发和部署 AWS 时保持透明和谨慎，以避免我们在论文中强调的对其全球稳定和 AI 研究产生的负面影响。
URL
https://arxiv.org/abs/2405.01859
