  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.
