Human-centered AI development in practice—insights from a multidisciplinary approach

Abstract

Abstract Industry 5.0 will focus on human-centered digitalization and put people at the center of manufacturing. To achieve this, it is necessary to consider employees throughout the transformation process. For this purpose, a practical approach to human-centered AI integration based on the Design Science Research method is presented in this article. The main requirements of the approach were evaluated in interviews with ten pilot companies, especially SMEs. The practical procedure was then examined in more detail for three application examples. The results show that there is still a lack of a uniform approach regarding the involvement of employees. Information on AI projects often reach those affected only during implementation. Even though the interdisciplinary approach is understood as a feasible approach for holistic AI development and implementation, there is still a lack of practical guidelines for adopting this multi-perspective approach. This work provides an initial starting point for a practice-oriented approach to the introduction of human-centered AI. Practical Relevance : As a result of exponential digitalization and the associated in-depth use of partially to fully automated technologies such as AI, collaboration between humans and machines is becoming increasingly important. It is essential for companies to work according to human-centered approaches to consider motivation and satisfaction as well as aspects of data protection and personal protection for employees. The necessity for companies to adopt a human-centric and sustainable approach to digitalization will intensify in coming years. The work addresses the needs for operationalization of research findings for companies. Thus, the proposed guidelines can be understood as an approach to practical knowledge transfer from science to industry.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 27 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.