DefinePK

Abstract

This paper explores the ways in which industrial labor, production efficiency, and socio-technical systems are reinvented by AI-powered computer vision. Computer vision is changing the field of manufacturing and robotics through artificial intelligence (AI) and allowing a degree of automation, precision, and flexibility never seen before. In spite of the large-scale technological development, social and organizational consequences of these innovations are under-researched.  We position our research at the crossroads of the technological innovation and social science investigation, based on the recent research in the field of the AI application (Krizhevsky, Sutskever, and Hinton, 2017; LeCun, Bengio, and Hinton, 2015). With a qualitative synthesis of secondary sources, such as benchmark studies on convolutional neural networks (CNNs), reinforcement learning frameworks and robotics applications in the industry (Patel et al., 2019; Goodfellow, Bengio, and Courville, 2016), we evaluate the disruptive promise of vision-based AI in manufacturing. The evidence indicates that AI contributes to the improvement of defect detection, predictive maintenance, and human-robot cooperation, which eventually leads to the restructuring of the organization and changes the skills requirements of the workforce (Buxmann and Hinz, 2022). Our position is that AI computer vision is helping to boost operational efficiency, but it is also creating a number of socio-economic issues, such as job displacement, algorithmic obscurity and ethical risks. Such findings support the necessity to include the social science lens in strategies of AI adoption. We end our research with the recommendations on striking a balance between efficiency benefits and inclusive labor policies, regulatory policies, and responsible innovation.
Keywords : Artificial intelligence, computer vision, robotics, manufacturing, automation, socio-technical systems, ethical AI.