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 for the explanations to be 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：可解释技术正在快速发展，旨在改善各种协作工作场景中的人类-AI决策。因此，先前的研究通过考察适当的依赖关系和任务绩效，评估了决策者如何与不完美AI协作，以设计更加以人为本的计算机支持的协作工具。为了提高决策者与AI的协作效果，研究者提出了多种以人为本的可解释人工智能（XAI）技术。然而，这些技术基于先前主要关注错误AI建议影响的发现。很少有研究认识到，即使AI建议正确，解释也可能出现错误。因此，理解不完美XAI如何影响人类-AI决策至关重要。本研究进行了一项包含136名参与者的稳健的混合方法用户实验，评估在鸟类物种识别任务中，错误解释如何影响人类的决策行为，同时考虑其专业水平以及解释的断言性程度。我们的研究结果揭示了不完美XAI和人类专业水平对他们对AI的依赖以及人类-AI团队绩效的影响。我们还讨论了在人类-AI协作过程中，解释如何可能误导决策者。因此，本研究揭示了不完美XAI在计算机支持协作工作领域的影响，并为人类-AI协作系统的设计者提供了指导原则。