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.

翻译：人工智能的应用，特别是基于机器学习方法的应用，正在全球各行各业和社会中不断增长。人工智能的采用将如何推进，尤其是在国际安全领域？关于自动化偏差的研究表明，人类往往对人工智能过度自信；而关于算法厌恶的研究则显示，随着决策风险的上升，人类对算法变得更加谨慎。我们理论化地探讨了关于人工智能的背景知识、对人工智能的信任，以及这些因素如何与其他因素交互，从而影响国际安全背景下自动化偏差发生的概率。我们通过一项预先注册的任务识别实验来验证这些理论，实验样本涵盖9个不同人工智能产业发展水平的国家，共9000名成年人。实验结果强烈支持该理论，尤其是关于人工智能背景知识的部分。邓宁-克鲁格效应似乎在发挥作用：人工智能经验最低的群体略微更可能表现出算法厌恶，随后在较低知识水平时出现自动化偏差，并在受访者人工智能背景知识达到最高水平之前趋于平缓。其他结果显示，任务难度、整体人工智能信任度，以及决策辅助者（人类或人工智能）被描述为高度胜任还是较低胜任，也显著影响实验结果。