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Uses of artificial intelligence (AI), especially those powered by machinelearning approaches, are growing in sectors and societies around the world. Howwill AI adoption proceed, especially in the international security realm?Research on automation bias suggests that humans can often be overconfident inAI, whereas research on algorithm aversion shows that, as the stakes of adecision rise, humans become more cautious about trusting algorithms. Wetheorize about the relationship between background knowledge about AI, trust inAI, and how these interact with other factors to influence the probability ofautomation bias in the international security context. We test these in apreregistered task identification experiment across a representative sample of9000 adults in 9 countries with varying levels of AI industries. The resultsstrongly support the theory, especially concerning AI background knowledge. Aversion of the Dunning Kruger effect appears to be at play, whereby those withthe lowest level of experience with AI are slightly more likely to bealgorithm-averse, then automation bias occurs at lower levels of knowledgebefore leveling off as a respondent's AI background reaches the highest levels.Additional results show effects from the task's difficulty, overall AI trust,and whether a human or AI decision aid is described as highly competent or lesscompetent.

Bending the Automation Bias Curve: A Study of Human and AI-based Decision Making in National Security Contexts