mlsanityData to 14 Oct 2026

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

arXiv 2305.11581 ↗First posted 19 May 2023cs.AI econ.GN q-fin.ECcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 167 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

33 of 167 references.

References · 33

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21 more
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  1. 2024What if? Causal Machine Learning in Supply Chain Risk Managementcited by 1