  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.
