  Uses of artificial intelligence (AI), especially those powered by machine
learning approaches, are growing in sectors and societies around the world. How
will AI adoption proceed, especially in the international security realm?
Research on automation bias suggests that humans can often be overconfident in
AI, whereas research on algorithm aversion shows that, as the stakes of a
decision rise, humans become more cautious about trusting algorithms. We
theorize about the relationship between background knowledge about AI, trust in
AI, and how these interact with other factors to influence the probability of
automation bias in the international security context. We test these in a
preregistered task identification experiment across a representative sample of
9000 adults in 9 countries with varying levels of AI industries. The results
strongly support the theory, especially concerning AI background knowledge. A
version of the Dunning Kruger effect appears to be at play, whereby those with
the lowest level of experience with AI are slightly more likely to be
algorithm-averse, then automation bias occurs at lower levels of knowledge
before 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 less
competent.
