  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.
