Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
This research addresses the lack of empirical studies on trust in real-world AI applications, providing insights for developers and researchers, though it is incremental as it extends existing trust frameworks to a specific domain.
The study investigated how real end-users trust an AI-based bird identification app, finding that while participants generally perceived it as trustworthy, they selectively verified outputs and avoided adoption in high-stakes scenarios, with domain knowledge and context influencing trust decisions.
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.