mlsanityData to 14 Oct 2026

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

arXiv 2405.01859 ↗First posted 3 May 2024cs.CY cs.AI cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 4 of its 135 references that are in the map. First posted on arXiv 3 May 2024.

Builds on

4 of 135 references.

References · 4

  1. 2018Learning Dexterous In-Hand Manipulationcited by 687
  2. 2019Deep Dynamics Models for Learning Dexterous Manipulationcited by 206
  3. 2024Escalation Risks from Language Models in Military and Diplomatic Decision-Makingcited by 63
  4. 2021An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioningcited by 32

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 3.

13 papers.

Sort: