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Domain-specific intelligent systems are meant to help system users in theirdecision-making process. Many systems aim to simultaneously support differentusers with varying levels of domain expertise, but prior domain knowledge canaffect user trust and confidence in detecting system errors. While it is alsoknown that user trust can be influenced by first impressions with intelligentsystems, our research explores the relationship between ordering bias anddomain expertise when encountering errors in intelligent systems. In thispaper, we present a controlled user study to explore the role of domainknowledge in establishing trust and susceptibility to the influence of firstimpressions on user trust. Participants reviewed an explainable imageclassifier with a constant accuracy and two different orders of observingsystem errors (observing errors in the beginning of usage vs. in the end). Ourfindings indicate that encountering errors early-on can cause negative firstimpressions for domain experts, negatively impacting their trust over thecourse of interactions. However, encountering correct outputs early helps moreknowledgable users to dynamically adjust their trust based on theirobservations of system performance. In contrast, novice users suffer fromover-reliance due to their lack of proper knowledge to detect errors.

The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems