mlsanityData to 14 Oct 2026

Basic Research, Lethal Effects: Military AI Research Funding as Enlistment

arXiv 2411.17840 ↗First posted 26 Nov 2024cs.CY cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a 'one small problem' caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one's research would raise, by obscuring the connections that implicate researchers in U.S. militarism.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 83 references that are in the map. First posted on arXiv 26 Nov 2024.

Builds on

3 of 83 references.

References · 3

  1. 2020The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencecited by 195
  2. 2022Dislocated Accountabilities in the AI Supply Chain: Modularity and Developers' Notions of Responsibilitycited by 33
  3. 2023To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processingcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.