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：解释性技术正迅速发展，以改善各种协作工作场景中的人机决策。因此，以往研究通过评估决策者与不完美AI的合作中的合理依赖和任务表现，旨在设计更人性化的计算机支持协作工具。为提升决策者与AI的协作，人们提出了多种以人为中心的可解释人工智能（XAI）技术；然而，这些技术基于以往主要关注错误AI建议影响的研究发现。很少有研究承认，即使AI建议正确，解释也可能出错。因此，理解不完美XAI如何影响人机决策至关重要。本研究通过一项包含136名参与者的稳健、混合方法用户研究，评估在鸟类物种识别任务中，错误解释如何根据参与者的专业水平和解释的确定性程度影响人类的决策行为。我们的研究结果揭示了不完美XAI和人类专业水平对决策者依赖AI以及人机团队表现的影响。我们还讨论了在人类与AI协作过程中，解释如何误导决策者。因此，我们阐明了不完美XAI在计算机支持协作工作领域的影响，并为人类-AI协作系统的设计者提供了指导准则。