Fairness is an essential consideration for most processes in an organization since an equitable treatment of people involved in a process is often mandated by the rules or regulations. It is also desired from a social sustainability perspective. Many processes have a social impact on the actors performing the process activities and on the subjects affected by the process. We focus on the latter case in which a group of process subjects, such as citizens or patients, experiences unfair bias or discrimination during the execution of the process. Obvious instances of such discrimination in processes are negative decisions, but any change in process behavior for a certain group may be a symptom of unfairness. Process mining has been proposed as a method to analyze such unfairness. However, when considering the classical process discovery of a single overall process model, such hidden biases may get disregarded since they are relatively rare occurrences. To address unfairness in processes through process mining, we first need to reveal it in the process model. Towards this goal, we contribute a fairness-aware process discovery approach that extends a genetic algorithm with new quality measures for group fairness. We tested the approach on a set of synthetic but realistic benchmark datasets containing controlled cases of unfairness. The results indicate that in several cases our approach succeeds in revealing hidden biases against certain groups, which would remain hidden in state-of-the-art process discovery. We consider this as an initial step towards a comprehensive analysis of unfairness in processes.