2024/03/14 by Michael C. Horowitz, Michael C Horowitz, Lauren Kahn · 49 citations
Engineering · Health Professions · Psychology · Social Sciences · #Automation #Bending #Computer science #Engineering #Ethics and Social Impacts of AI #Human-Automation Interaction and Safety #Mechanical engineering #Occupational Health and Safety Research #Structural engineering
paper · doi:10.1093/isq/sqae020
published in International Studies Quarterly 68(2) (Oxford University Press)
openalex publication_date 2024/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
Abstract Uses of artificial intelligence (AI) are growing around the world. What will influence AI adoption 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 9,000 adults in nine 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.