Problem
Research questions and friction points this paper is trying to address.
automation bias
algorithm aversion
human judgment
AI-enabled decision-support systems
military decision-making
What is Human in Judgment? Testing Automation Bias and Algorithm Aversion Among United States Military Academy Cadets
🤖 AI Summary
This study investigates whether military personnel are more susceptible to automation bias or algorithm aversion when using AI decision-support systems, potentially impairing battlefield judgment. Conducting a controlled experiment with cadets from the United States Military Academy at West Point and members of the general public, the research examines participants’ trust in and subsequent adjustment of recommendations provided by either an algorithm or a human analyst during a target identification task. As the first empirical investigation of these cognitive biases within an authentic military educational context, the study integrates survey-based measures, behavioral data, and cross-group analysis. Findings indicate that military training fosters calibrated trust in AI advice: West Point cadets exhibited significantly less susceptibility to both automation bias and algorithm aversion compared to civilian participants, demonstrating a more rational and appropriately tempered level of trust.
📝 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 completed a target identification task and then received advice from either an algorithm or a human analyst and had the opportunity to re-assess their initial identification, allowing direct measurement of automation bias and algorithm aversion. Contrary to prominent concerns, we find that West Point cadets are less prone to cognitive distortion than members of the general public, displaying better calibrated trust in algorithmic decision support systems. While the findings are limited, they suggest that military education and exposure to AI can meaningfully shape how AI influences international politics in matters of war and peace.
Innovation
Methods, ideas, or system contributions that make the work stand out.
automation bias
algorithm aversion
AI-enabled decision-support systems
military judgment
human-AI interaction
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