What shapes warfighters’ trust in military technologies augmented with Artificial Intelligence (AI)? Research often assumes that junior military personnel, including cadets training to become officers, will trust AI during future wars, and at higher levels than senior officers. We test these claims by fielding a survey experiment among a representative sample of cadets assigned to the Reserve Officers’ Training Corps (ROTC) program in the United States. Our analysis reveals that cadets are more trusting of AI-enhanced military technologies than senior officers, but that their trust is shaped by a more conservative understanding of the appropriate use and oversight of AI. We also find that cadets’ trust is shaped by a complex set of instrumental, normative, and operational considerations, including ongoing cognitive development, education, and professional enculturation. These results provide the first experimental evidence of cadets’ trust in AI-enhanced military technologies and have implications for future research, policy, and military modernization.
AbbasH.ScholzJ.ReidD. (Eds.). (2018). Foundations of trusted autonomy. Springer.
2.
AraiK.MatsumotoM. (2023). Public perceptions of autonomous lethal weapons systems. AI and Ethics, 1–12.
3.
ArainM.HaqueM.JohalL.MathurP.NelW.RaisA.SandhuR.SharmaS. (2013). Maturation of the human brain. Neuropsychiatric Disease and Treatment, 1(20), 449–461.
4.
AshbyW. R. (1956). Introduction to cybernetics. John Wiley and Sons.
5.
BairdD.EsquivelJ.FlottN.StandishT.Van OrdenB. (2023). The rise of the digital native: How the next generation of analysts and technology are changing the intelligence landscape. U.S. Army War College Press.
6.
BellA. (2024). Can IHL training influence military conduct in war?International Committee of the Red Cross.
7.
BowenT.GoldfienM.GrahamM. (2022). Public opinion and nuclear use: Evidence from factorial experiments. Journal of Politics, 85(1), 345–350.
8.
BradshawJ. M.HoffmanR. R.WoodsD. D.JohnsonM. (2013). The seven deadly myths of “autonomous systems.”IEEE Intelligent Systems, 28(3), 54–61.
9.
BrutgerJ. D.KertzerJ.Renshon Dustin Tingley ChagaiM. (2022). Abstraction and detail in experimental design. American Journal of Political Science, 67(4), 979–995.
10.
DillJ.SchubigerL. (2021). Attitudes toward the use of force: Instrumental imperatives, moral principles, and international law. American Journal of Political Science, 65(3), 612–633.
11.
DrennanJ. E. (2018). How to fight an unmanned war. In JacksonJ. E. (Ed.), Naval innovation (pp. 117–127). Naval Institute Press.
12.
DruckmanJ. N. (2022). Experimental thinking: A primer on social science experiments. Cambridge University Press.
13.
ErskineT.MillerS. E. (2024). AI and the decision to go to war: Future risks and opportunities. Australian Journal of International Affairs, 78(2), 135–147.
14.
FacerK.FurlongR. (2001). Beyond the myth of the ‘Cyberkid’: Young people at the Margins of the Information Revolution. Journal of Youth Studies, 4(4), 451–469.
15.
FlournoyM. (2023). AI is already at war: How artificial intelligence will transform the military. Foreign Affairs.
16.
GalliottJ.Baggiarini RupkaS. (2021). Empirical data on attitudes toward autonomous systems. In GalliottJ.MacIntoshD.OhlinJ. D. (Eds.), Lethal autonomous weapons: Re-examining the law and ethics of robotics warfare (pp. 137–158). Oxford University Press.
17.
GalliottJ.WyattA. (2022). A consideration of how emerging military leaders perceive themes in the autonomous weapon system discourse. Defence Studies, 22(2), 253–276.
18.
GoecksV. G.WaytowichN. (2024). COA-GPT: Generative pre-trained transformers for accelerated course of action development in military operations. ArXiv.
19.
GoldfarbA.LindsayJ. (2022). Prediction and judgment: Why artificial intelligence increases the importance of humans in war. International Security, 46(3), 7–50.
20.
GraysonT. (2023). DARPA tiles together a vision of mosaic warfare. Defense Advanced Research Projects Agency.
21.
HarsgorL. (2024). The young and the hawkish: Generational differences in conflict attitudes in Israel. Research & Politics, 11(2), 1–9.
22.
HawleyJ. K. (2017). Patriot wars: Automation and the patriot air and missile defense system. Center for a New American Security.
23.
HelsperE.EynonR. (2010). Digital natives? Where is the evidence?British Educational Research Journal, 36(3), 503–520.
24.
HorowitzM.Lin-GreenbergE. (2022). Algorithms and influence artificial intelligence and crisis decision-making. International Studies Quarterly, 66(4), 1–11.
25.
HunterC.BownB. (2024). We’ll never have a model of an AI major-general: Artificial intelligence, command decisions, and kitsch visions of war. Journal of Strategic Studies, 47(1), 116–146.
26.
HusainA.AllenJ.WorkR.ColeA.PorterB.AndersonW.TownsendJ. (2018). Hyperwar: Conflict and competition in the AI century. SparkCognition Press.
27.
JostT.MeshkinK.SchubR. (2022). The character and origins of military attitudes on the use of force. International Studies Quarterly, 66(2), 1–10.
KallenbornZ. (2023). Policy makers should plan for superintelligent AI, even if it never happens. Bulletin of the Atomic Scientists.
30.
KaneJ. V. (2024). More than meets the ITT: A guide for anticipating and investigating nonsignificant results in survey experiments. Journal of Experimental Science, 1–16. https://doi.org/10.1017/XPS.2024.1
31.
KertzerJ. (2017). Microfoundations in international relations. Conflict Management and Peace Science, 34(1), 81–97.
32.
KrepsS.GeorgeJ.LushenkoP.RaoA. (2023). Exploring the artificial intelligence “trust paradox”: Evidence from a survey experiment in the United States. PLOS ONE, 18(7), 1–21.
33.
KrepsS.WallaceG. P. R. (2016). International law, military effectiveness, and public support for drone warfare. Journal of Peace Research, 53(6), 830–844.
34.
KuoK. (2022). Dangerous changes: When military innovation harms combat effectiveness. International Security, 47(2), 48–87.
35.
LauxJ.WachterS.MittelstadtB. (2024). Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk. Regulation & Governance, 18, 3–32.
36.
LeeH.ZoH. (2017). Assimilation of military group decision support systems in Korea: The mediating role of structural appropriation. Information Development, 33(1), 14–28.
37.
LeleA. (2019). Disruptive technologies for the militaries and security. Springer.
38.
LindsayJ. (2020). Information technology & military power. Cornell University Press.
39.
LindsayJ. (2023). War is from Mars, AI is from Venus: Rediscovering the institutional context of military automation. Texas National Security Review, 7(1), 1–33.
40.
Lin-GreenbergE. (2023). Evaluating escalation: Conceptualizing escalation in an era of emerging Military technologies. Journal of Politics, 85(3), 1151–1155.
41.
LonerganE. (2024). Emerging technology and the cult of the offensive. Contemporary Security Policy, 45(3), 459–493.
42.
LushenkoP. (2023). AI & the future of warfare. Bulletin of the Atomic Scientists.
43.
LushenkoP.RamanS. (2024). The legitimacy of drone warfare: Evaluating public perceptions. Routledge.
44.
MajnemerJ.MeibauerG. (2023). Names from nowhere? Fictitious Country names in survey vignettes affect experimental results. International Studies Quarterly, 6(1), 1–10.
45.
MayerM. (2023). Trusting machine intelligence: Artificial intelligence and human-autonomy teaming in military operations. Defense & Security Analysis, 39(4), 1–19.
46.
MontagC.Klugah-BrownB.ZhouX.WernickeJ.LuiC.KouJ.ChenY.HassB.BeckerB. (2023). Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging. Personality Neuroscience, 6(3), 1–8.
47.
MutzD. (2011). Population-based survey experiments. Princeton University Press.
48.
PakR.RoviraE.McLaughlinA.BaldwinN. (2016). Does the domain of technology impact user trust? Investigating trust in automation across different consumer-oriented domains in young adults, military, and older adults. Theoretical Issues in Ergonomics Science, 18(3), 199–220.
49.
ParkD.BischofG. (2013). The aging mind: Neuroplasticity in response to cognitive training. Dialogues of Clinical Neuroscience, 15(1), 109–119.
50.
PayneK. (2021). I, warbot: The dawn of artificially intelligence conflict. Hurst & Company.
51.
PrenskyM. (2001a). Digital natives, digital immigrants part 1. On the Horizon, 9(5), 1–6.
52.
PrenskyM. (2001b). Digital natives, digital immigrants part 2: Do they really think differently?On the Horizon, 9(6), 1–6.
53.
RiveraJ. P.MukobiG.ReuelA.LamparthM.SmithC.ScheiderJ. (2024). Escalation risks from language models in military and diplomatic decision-making. Arxiv.org.
54.
RosenS. P. (1988). New ways of war: Understanding military innovation. International Security, 13(1), 134–168.
55.
RosenS. P. (1994). Winning the next war: Innovation and the modern military. Cornell University Press.
56.
RosendorfO.SmetanaM.VrankaM. (2022). Autonomous weapons and ethical judgments: Experimental evidence on attitude toward the military use of “killer robots.”Peace and Conflict: Journal of Peace Psychology, 28(2), 177–183.
57.
RosendorfO.SmetanaM.VrankaM. (2023). Algorithmic aversion? Experimental evidence on the elasticity of public attitudes to “Killer Robots.”Security Studies, 33(1), 1–31.
58.
RossiterA.LaytonP. (2024). Warfare in the robotics age. Lynne Rienner Publishers.
59.
ScharreP. (2016). Centaur warfighting: The false choice of humans vs. automation. Temple International & Comparative Law Journal, 30, 151–165.
60.
ScharreP. (2021). Debunking the AI arms race theory. Texas National Security Review, 4(3), 121–132.
61.
ScharreP. (2024). The perilous coming age of AI warfare: How to limit the threat of autonomous weapons. Foreign Affairs.
SchneiderJ.MacdonaldJ. (2016). U.S. public support for drone strikes. Center for New American Security.
64.
SheagleyG.CliffordS. (2023). No evidence that measuring moderators alters treatment effects. American Journal of Political Science. Advance online publication. https://doi.org/10.1111/ajps.12814
65.
Sinnott-ArmstrongW. (2008). Framing moral intuitions. In Sinnott-ArmstrongW. (Ed.), Moral psychology, volume 2, the cognitive science of morality: Intuition and diversity (pp. 47–77). The MIT Press.
66.
SparrowR.HenschkeA. (2023). Minotaurs, not centaurs: The future of manned-unmanned teams. Parameters, 53(1), Article 14.
67.
StoetzerL.ZhouX.SteenbergenM. (2024). Casual inference with latent outcomes. American Journal of Political Science. Advance online publication. https://doi.org/10.1111/ajps.12871
68.
VoldK. (2024). Human-AI cognitive teaming: Using AI to support state-level decisionmaking on teh resort to force. Australian Journal of International Affairs, 78(2), 229–236
69.
WoodcockT. (2023). Human/machine(-learning) interactions, human agency and the international humanitarian law proportionality standard. Global Society, 38(1), 100–121.
70.
Yarhi-MiloK. (2014). Knowing the adversary: Leaders, intelligence, and assessment of intentions in international relations. Princeton University Press.
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