Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supported collaborative tools. Several human-centered explainable AI (XAI) techniques have been proposed in hopes of improving decision-makers' collaboration with AI; however, these techniques are grounded in findings from previous studies that primarily focus on the impact of incorrect AI advice. Few studies acknowledge the possibility of the explanations being incorrect even if the AI advice is correct. Thus, it is crucial to understand how imperfect XAI affects human-AI decision-making. In this work, we contribute a robust, mixed-methods user study with 136 participants to evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task, taking into account their level of expertise and an explanation's level of assertiveness. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance. We also discuss how explanations can deceive decision-makers during human-AI collaboration. Hence, we shed light on the impacts of imperfect XAI in the field of computer-supported cooperative work and provide guidelines for designers of human-AI collaboration systems.

翻译：可解释性技术正迅速发展，旨在提升跨各类协作工作场景中人类与AI联合决策的效果。为此，先前研究通过考察决策者对不完美AI的合理依赖及任务表现，评估其协作方式，以期设计更以人为本的计算机支持协作工具。学界已提出多种以人为本的可解释人工智能（XAI）技术，旨在改善决策者与AI的协作；然而，这些技术基于以往主要关注错误AI建议影响的研究结论。很少有研究认识到，即使AI建议正确，其解释仍可能出错的可能性。因此，理解不完美XAI如何影响人类-AI决策至关重要。本研究通过一项包含136名参与者的稳健混合方法用户实验，评估在鸟类物种识别任务中，错误解释如何影响人类决策行为，同时考虑参与者的专业水平与解释的肯定程度。我们的发现揭示了不完美XAI与人类专业水平对其AI依赖度及人机团队表现的影响。此外，我们探讨了解释在人机协作中如何误导决策者。因此，本研究阐明了不完美XAI在计算机支持协作工作领域的影响，并为人类-AI协作系统设计者提供了指导准则。