  Despite the proliferation of explainable AI (XAI) methods, little is
understood about end-users' explainability needs and behaviors around XAI
explanations. To address this gap and contribute to understanding how
explainability can support human-AI interaction, we conducted a mixed-methods
study with 20 end-users of a real-world AI application, the Merlin bird
identification app, and inquired about their XAI needs, uses, and perceptions.
We found that participants desire practically useful information that can
improve their collaboration with the AI, more so than technical system details.
Relatedly, participants intended to use XAI explanations for various purposes
beyond understanding the AI's outputs: calibrating trust, improving their task
skills, changing their behavior to supply better inputs to the AI, and giving
constructive feedback to developers. Finally, among existing XAI approaches,
participants preferred part-based explanations that resemble human reasoning
and explanations. We discuss the implications of our findings and provide
recommendations for future XAI design.
