The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems
This research addresses trust calibration in intelligent systems for users with varying expertise, but it is incremental as it builds on known effects of first impressions and domain knowledge.
The study investigated how domain expertise and the order of encountering errors affect user trust in an explainable image classifier, finding that early errors negatively impact experts' trust while novices over-rely on the system due to inability to detect errors.
Domain-specific intelligent systems are meant to help system users in their decision-making process. Many systems aim to simultaneously support different users with varying levels of domain expertise, but prior domain knowledge can affect user trust and confidence in detecting system errors. While it is also known that user trust can be influenced by first impressions with intelligent systems, our research explores the relationship between ordering bias and domain expertise when encountering errors in intelligent systems. In this paper, we present a controlled user study to explore the role of domain knowledge in establishing trust and susceptibility to the influence of first impressions on user trust. Participants reviewed an explainable image classifier with a constant accuracy and two different orders of observing system errors (observing errors in the beginning of usage vs. in the end). Our findings indicate that encountering errors early-on can cause negative first impressions for domain experts, negatively impacting their trust over the course of interactions. However, encountering correct outputs early helps more knowledgable users to dynamically adjust their trust based on their observations of system performance. In contrast, novice users suffer from over-reliance due to their lack of proper knowledge to detect errors.