Traumer, FabianFabianTraumerOeste-Reiß, SarahSarahOeste-ReißLeimeister, Jan MarcoJan MarcoLeimeister2023-04-132023-04-132017https://www.alexandria.unisg.ch/handle/20.500.14171/103529In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy.enMachine LearningTaskInteractionCollaborationCrowdsourcingTaxonomyconference paper