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

Domain-specific intelligent systems are meant to help system users in their\ndecision-making process. Many systems aim to simultaneously support different\nusers with varying levels of domain expertise, but prior domain knowledge can\naffect user trust and confidence in detecting system errors. While it is also\nknown that user trust can be influenced by first impressions with intelligent\nsystems, our research explores the relationship between ordering bias and\ndomain expertise when encountering errors in intelligent systems. In this\npaper, we present a controlled user study to explore the role of domain\nknowledge in establishing trust and susceptibility to the influence of first\nimpressions on user trust. Participants reviewed an explainable image\nclassifier with a constant accuracy and two different orders of observing\nsystem errors (observing errors in the beginning of usage vs. in the end). Our\nfindings indicate that encountering errors early-on can cause negative first\nimpressions for domain experts, negatively impacting their trust over the\ncourse of interactions. However, encountering correct outputs early helps more\nknowledgable users to dynamically adjust their trust based on their\nobservations of system performance. In contrast, novice users suffer from\nover-reliance due to their lack of proper knowledge to detect errors.\n