Andreswari, RachmaditaLaue, RalfFahrenkrog-Petersen, Stephan2024-05-082024-05-082024978-3-88579-743-21617-5468https://dl.gi.de/handle/20.500.12116/44014Process mining techniques provide operational insights into work processes in various types of organizations. These processes handle sensitive data of customers, patients, students, or citizens and their results impact the lives and careers of these affected persons. So far, much of process mining research has focused on classical dimensions of performance such a cycle time or operational cost. What is missing is a primal consideration of ethical concerns such as fairness. Fairness is a recently researched concept in machine learning, which requires a deeper integration into process mining algorithms. This research addresses this requirement. To this end, it aims to analyze fairness concerns in process mining, to develop new process mining algorithms that integrate fairness concerns, and to evaluate them for their effectiveness. Methodologically, our research will build on guidelines for design science and algorithm engineering research. In this way, we will combine engineering research with empirical evaluations.enprocess miningfairnessalgorithmwork process10.18420/EMISA2024_02