HyMeKI

Advances in the field of artificial intelligence, (especially machine learning and speech recognition), offer new design options for reorganizing knowledge work at the interface between humans and AI. AI systems not only provide potential in the automation of routine tasks but can also support the solution of complex tasks of employees* as new "team members" since they contribute complementary skills to humans in many areas. People perceive AI-based systems as social actors, but therefore have similar expectations regarding the quality of their solution contributions and their communication behavior. These expectations are often not met and can lead to dissatisfaction, rejection, or non-use of the systems. The differences in the abilities and skills of humans (i.e. human intelligence) and machines (i.e. artificial intelligence) create new design challenges in cooperation (hybrid intelligence systems) and in the learning processes for human and machine learning.

The junior research group aims to develop, test, and validate socio-technical design requirements and patterns for the development of AI systems in knowledge work. These implement collaborative working practices of human-AI cooperation, particularly for the division of labor, for the transparent, comprehensible transfer of tasks and work statuses, and for promoting learning between humans and AI systems according to their respective strengths.

Representative collaboration scenarios in knowledge work are surveyed and modeled in an application-oriented manner by empirical requirements elicitation with companies to achieve the objectives. Based on this, the junior research group develops a taxonomy for labor division between humans and AI systems. Besides, techniques for transfer orchestration between humans and AI as well as techniques for the promotion of AI (or human-) supported human (or machine) learning are explored and transferred into design patterns. The developed techniques and design patterns are prototypically instantiated and socio-technically evaluated in the laboratory, field, and online studies. The project thus follows a design-oriented multi-method approach of iterative development and evaluation.

The subproject "Techniques for the promotion of AI-supported human learning as well as human-supported AI learning in the context of knowledge work" comprises one of two research foci of the junior research group and is led by the University of Kassel. The subproject aims to explore techniques for AI-supported human learning (AI trains humans) as well as techniques for human-supported AI learning (humans train AI) in the application field of knowledge work. By analyzing learning effects in the form of knowledge growth in the laboratory and the field using qualitative and quantitative methods, and to derive design patterns. The collected findings are incorporated into the two cross-sectional topics - i.e. a taxonomy for the division of work and tasks between humans and AI as well as a design pattern catalog, by the AI junior research group.

• University of Kassel, Department of Information Systems, Dr. Sarah Oeste-Reiß (junior research group leader), Prof. Dr. Jan Marco Leimeister (mentor)
• University of Kassel, Department of Intelligent Embedded Systems, Prof. Dr. Bernhard Sick (Mentor)
• University of Hamburg, Information Systems, Socio-technical Systems Design, Prof. Dr. Eva Bittner (Junior Research Group Leader)

Value Partners

• IHK Hessen innovativ
• Aiconix GmbH, Hamburg
• smartransfer, Kassel

The project is funded within the framework of the BMBF guideline "Promotion of female AI junior scientists". The applicants had to pass a two-stage, competitive selection procedure. A total of twenty projects were selected nationwide, which are now being funded.
 
Funding code of the University of Kassel: 01IS20057B

Project Website

www.hymeki.de

• Dr. Sarah Oeste-Reiß (Junior Research Group Leader)
• Prof. Dr. Jan Marco Leimeister (Mentor)