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

arXiv: https://arxiv.org/abs/2306.16507 · HTML: https://mlsanity.com/paper/2306.16507/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [Standard Language Ideology in AI-Generated Language](https://mlsanity.com/paper/2406.08726/) (2024)
- [Bias in the Loop: How Humans Evaluate AI-Generated Suggestions](https://mlsanity.com/paper/2509.08514/) (2025)
- [The Psychology of Learning from Machines: Anthropomorphic AI and the Paradox of Automation in Education](https://mlsanity.com/paper/2601.06172/) (2026)
- [Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework](https://mlsanity.com/paper/2603.18677/) (2026)
- [Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts](https://mlsanity.com/paper/2603.01457/) (2026)
- [The Competence Shadow: Theory and Bounds of AI Assistance in Safety Engineering](https://mlsanity.com/paper/2603.25197/) (2026)

## Abstract (arXiv)

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.