Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions

TL;DR

This paper examines the integration of trustworthy, ethical, and responsible AI in manufacturing, highlighting risks, challenges, and proposing research questions to guide future safe and responsible AI deployment.

Contribution

It provides a synthesis of current frameworks, interprets AI trustworthiness in manufacturing, and introduces research questions to address practical implementation challenges.

Findings

Identifies key risks associated with AI in manufacturing.

Maps AI trustworthiness concerns to the machine learning lifecycle.

Proposes research questions to guide future responsible AI research.

Abstract

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.