What is Human in Judgment? Comparing Automation Bias and Algorithm Aversion Between the United States Military Academy and the General Public
Lauren Kahn, Michael C. Horowitz, Laura Resnick Samotin

TL;DR
This study compares how US military cadets and the general public interact with AI decision support, revealing cadets are less prone to automation bias and better calibrated in trusting AI.
Contribution
It provides empirical evidence that military training and exposure to AI can reduce automation bias and improve trust calibration in AI systems.
Findings
US military cadets show less automation bias than the general public.
Cadets demonstrate better trust calibration in AI decision support.
Military education influences perceptions and trust in AI during conflict scenarios.
Abstract
Human judgment has always been central to conflict and escalation, but how will a world of artificial intelligence (AI) change the role of humans in war? As militaries increasingly adopt AI-enabled decision-support systems (DSS), including the United States in the war against Iran, concerns about automation bias -- over-reliance on algorithmic recommendations -- and algorithm aversion -- premature distrust of automated outputs -- raise fears that relying on AI too much could increase the risk of error, miscalculation, and accidents. Yet existing evidence on how militaries actually interact with AI remains limited. We test theories about the susceptibility of militaries to automation bias by comparing the results from a survey experiment conducted with 236 cadets at the United States Military Academy at West Point to a demographically similar cross-national public sample. Respondents…
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