压力与可解释AI影响人类采纳AI建议的行为
How Performance Pressure Influences AI-Assisted Decision Making
- 通过金钱激励和时间限制施加压力，调控人类对AI建议的采纳
- 不同压力组合下，采纳率最高提升23%，但部分组合反而降低信任度
- 适合研究人机协作、心理机制或设计决策辅助系统的人参考
许多领域已采用基于AI的决策辅助系统，尽管其潜力备受讨论，但因对AI的（不）信任及认为AI无法完成主观任务等原因，人机协作常表现不佳。本文研究了绩效压力在人机决策中的作用，采用低风险任务（垃圾邮件分类）进行实验，通过调整经济激励和设定时间限制来施加压力。结果表明，压力与可解释AI（XAI）技术存在复杂交互效应：某些组合能显著提升对AI建议的采纳率（最高达23%），而另一些组合则导致行为恶化。研究揭示了压力对人机协作的影响机制，并提出有效运用压力的策略，呼吁未来研究纳入压力分析。
原文摘要 · Abstract (English)
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.
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