The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks

Artificial intelligence (AI) outperforms humans in plentiful domains. Despite security and ethical concerns, AI is expected to provide crucial improvements on both personal and societal levels. However, algorithm aversion is known to reduce the effectiveness of human-AI interaction and diminish the potential benefits of AI. In this paper, we built upon the Dual System Theory and investigate the effect of the AI response time on algorithm aversion for slow-thinking and fast-thinking tasks. To answer our research question, we conducted a 2 $$\,\times \,$$ 2 incentivized laboratory experiment with 116 students in an advice-taking setting. We manipulated the length of the AI response time (short vs. long) and the task type (fast-thinking vs. slow-thinking). Additional to these treatments, we varied the domain of the task. Our results demonstrate that long response times are associated with lower algorithm aversion, both when subjects think fast and slow. Moreover, when subjects were thinking fast, we found significant differences in algorithm aversion between the task domains.