  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.
