A Conceptual Framework for Establishing Trust in Real World Intelligent Systems

Abstract: 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. Keywords: Intelligent Systems, AI, Trust, Explainable AI, Knowledge Management, Knowledge Patterns 1. INTRODUCTION uncommon and have been constructed in uncommon ways. Such techniques, a class to which systems that we now Human expertise in many aspects is largely based on call intelligent systems belong to, produce results of high prior knowledge and familiar patterns, which have either complexity (e.g.