More and more companies are using data as a basis for important decisions. In the meantime, however, the data volumes are so large and complex that they are no longer manageable for a human. AI systems offer a possibility here to process the accumulating data more efficiently.
However, in order to develop these AI systems, the necessary knowledge of domain experts must first be transferred to the AI through labeled data. However, this process is prone to quality loss due to biases. These are systematically erroneous perceptions judgments and actions. These can be transferred unconsciously when labeling the data and thus lead to biased decision-making processes of the AI systems.
Therefore, the goal is to develop and implement the interaction between humans and AI in hybrid intelligence systems to reduce bias and noise and to aggregate knowledge.