Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.04333 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910192548970496 |
|---|---|
| author | Kahn, Lauren Horowitz, Michael C. Samotin, Laura Resnick |
| author_facet | Kahn, Lauren Horowitz, Michael C. Samotin, Laura Resnick |
| contents | 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. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04333 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | What is Human in Judgment? Comparing Automation Bias and Algorithm Aversion Between the United States Military Academy and the General Public Kahn, Lauren Horowitz, Michael C. Samotin, Laura Resnick Computers and Society 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. 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. |
| title | What is Human in Judgment? Comparing Automation Bias and Algorithm Aversion Between the United States Military Academy and the General Public |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2604.04333 |