Reviewing Human-Centric Aspects of AI Collaboration in Manufacturing

Paper i proceeding, 2027

The increasing integration of artificial intelligence (AI) in manufacturing is reshaping human roles on the shop floor. While AI has the potential to enhance human capabilities, questions arise about its impact on workers. Ensuring human-centricity remains crucial, but there is a need to investigate how these principles translate to human-AI collaboration. This paper presents a structured literature review of 25 papers, examining human-centric goals, challenges and enablers in human-AI collaboration. The results show that the most emphasized goals include trust, transparency, augmentation, explainability, safety, and well-being. Prominent challenges include job displacement, conflicting priorities, and algorithmic bias. Identified enablers include co-design approaches, explainable interfaces, and dynamic task allocation. The findings highlight that human-centric aspects are interconnected, requiring a holistic approach. Although human-centricity is widely advocated, it is often framed at the system interface level, rather than in organizational or design processes. Future research should deepen the understanding of cognitive and psychosocial dimensions of human-AI collaboration and develop more empirical and longitudinal insights.