Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user’s set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks.

包含紧急元素的智能信息系统经常会遇到信任问题，因为对结果的解释不够充分，而且过程本身也无法完全追溯。这是由取决于随机元素或取决于输入数据的结构和相关性的控制流引起的。可以通过让用户与系统交互来建立对此类算法的信任，以便他们可以探索结果并找到可以与他们的预期解决方案进行比较的模式。根据算法结果反映人类对域的理解的特征和模式可以使人们意识到这种模式，并可以增加用户对解决方案的信任。如果未达到期望，则可以使用仔细检查来确定解决方案是否符合期望或超出期望。通过接受或拒绝解决方案，用户的期望集会演变，并为用户建立学习过程。在本文中，我们提出了一个反映并支持这一过程的概念框架。该框架是对来自两个不同学科的两个示例性案例研究进行分析的结果，该案例研究使用了有助于专家完成复杂任务的信息系统。