  Trust is an important factor in people's interactions with AI systems.
However, there is a lack of empirical studies examining how real end-users
trust or distrust the AI system they interact with. Most research investigates
one aspect of trust in lab settings with hypothetical end-users. In this paper,
we provide a holistic and nuanced understanding of trust in AI through a
qualitative case study of a real-world computer vision application. We report
findings from interviews with 20 end-users of a popular, AI-based bird
identification app where we inquired about their trust in the app from many
angles. We find participants perceived the app as trustworthy and trusted it,
but selectively accepted app outputs after engaging in verification behaviors,
and decided against app adoption in certain high-stakes scenarios. We also find
domain knowledge and context are important factors for trust-related assessment
and decision-making. We discuss the implications of our findings and provide
recommendations for future research on trust in AI.
