Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions
This work targets manufacturing practitioners and researchers by synthesizing existing frameworks to address AI risks, but it is incremental as it adapts known concepts without introducing new methods or data.
The paper addresses the lack of understanding of AI risks in manufacturing, exploring how responsible, ethical, and trustworthy AI principles apply to this sector and proposing research questions to guide future work for safe and beneficial AI deployment.
While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Although various high level frameworks and definitions have been proposed to consolidate potential risks, practitioners struggle with understanding and implementing them. This lack of understanding exposes manufacturing to a multitude of risks, including the organisation, its workers, as well as suppliers and clients. In this paper, we explore and interpret the applicability of responsible, ethical, and trustworthy AI within the context of manufacturing. We then use a broadened adaptation of a machine learning lifecycle to discuss, through the use of illustrative examples, how each step may result in a given AI trustworthiness concern. We additionally propose a number of research questions to the manufacturing research community, in order to help guide future research so that the economic and societal benefits envisaged by AI in manufacturing are delivered safely and responsibly.