论文标题
域专业知识在用户信任中的作用以及对智能系统的第一印象的影响
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems
论文作者
论文摘要
特定于领域的智能系统旨在帮助系统用户进行决策过程。许多系统旨在同时支持具有不同级别的域专业知识的不同用户，但是先前的域知识会影响用户信任和对检测系统错误的信心。虽然还知道，用户信任可能会受到智能系统的第一印象的影响，但我们的研究探讨了在遇到智能系统中遇到错误时订购偏见与域专业知识之间的关系。在本文中，我们提出了一项受控的用户研究，以探讨领域知识在建立信任和对第一印象对用户信任影响的影响的敏感性中的作用。参与者以恒定的精度和观察系统错误的两个不同顺序进行了可解释的图像分类器（在使用开始时观察错误与最终观察错误）。我们的发现表明，早期遇到错误可能会对领域专家造成负面印象，从而对他们在互动过程中的信任产生负面影响。但是，尽早遇到正确的输出有助于更多知识渊博的用户根据他们对系统性能的观察来动态调整其信任。相比之下，新手用户由于缺乏适当的知识来检测错误而遭受过度依赖的困扰。
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.
