AIXPERIMENTATIONlab (Augmented Intelligence Experimentation Laboratory - Augmented Intelligence for employee support in decision-making situations)

 

AIXPERIMENTATIONlab (Augmented Intelligence Experimentation Laboratory – Augmented Intelligence for Worker Decision Support) is a third party funded project for the design, development, usage, and diffusion of human-centered applications of Artificial Intelligence (AI).

The key objective is to combine the benefits of AI methods with the advantages of human judgement and to reduce the psychological work stress for employees within service departments. To achieve that, the chosen research approach is transdisciplinary and transforming: Scientists of RWTH Aachen University and Augsburg University of Applied Sciences conduct research in cooperation with employees and executives from the participating partner companies (HEIM & HAUS Bauelemente Produktionsgesellschaft mbH, HUH; Aumüller Aumatic GmbH, AUM; aixtema GmbH, AIX) and associates of the labour union ver.di.

The laboratory is characterized by three replicated workstations representing the partner companies. Because of the consistently growing amount of data (product data, accompanying data, external data, customer data) employees are already overwhelmed by the available information. In addition, they are confronted with external factors like customer expectations of fast response times, which are often perceived as stressful. Altogether, these trends are leading to a higher level of work stress, which is evident especially in short-term decision situations.
This problem can be addressed by a participative development of human-centered AI applications for decision support. The focus is to design an optimal human-AI interface from the employees’ perspective. The AI applications will be initially developed and tested in the AIXPERIMANTATIONlab followed by an implementation within the partner companies HUH, AUM and AIX. In this context, empirical data is collected regarding the effects of human-centered AI applications on the employees’ level of stress. The results and insights will be consolidated into a transformation concept considering all relevant aspects for the implementation of AI in operational work systems.