How Performance Pressure Influences AI-Assisted Decision Making
This work addresses the problem of underperformance in human-AI collaboration for decision-making, particularly in low-stakes tasks, by exploring pressure as a tool, though it is incremental in focusing on a specific factor.
The study investigated how performance pressure and explainable AI (XAI) techniques affect human advice-taking behavior in AI-assisted decision-making, using a spam review classification task with financial incentives and time limits, and found complex interaction effects that could either improve or worsen behavior.
Many domains now employ AI-based decision-making aids, and although the potential for AI systems to assist with decision making is much discussed, human-AI collaboration often underperforms due to factors such as (mis)trust in the AI system and beliefs about AI being incapable of completing subjective tasks. One potential tool for influencing human decision making is performance pressure, which hasn't been much studied in interaction with human-AI decision making. In this work, we examine how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior. Using an inherently low-stakes task (spam review classification), we demonstrate effective and simple methods to apply pressure and influence human AI advice-taking behavior by manipulating financial incentives and imposing time limits. Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior. We conclude by discussing the implications of these interactions, strategies to effectively use pressure, and encourage future research to incorporate pressure analysis.