Risk as a driver for AI framework development on manufacturing

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Date
2022-05-11
Authors
Vyhmeister, Eduardo
Gonzalez-Castane, Gabriel
Östbergy, P.-O.
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Springer
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https://doi.org/10.1007/s43681-022-00159-3
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Abstract
Incorporating ethics and values within the life cycle of an AI asset means to secure, under these perspectives, its development, deployment, use and decommission. These processes must be done safely, following current legislation, and incorporating the social needs towards having greater well-being over the agents and environment involved. Standards, frameworks and ethical imperatives—which are also considered a backbone structure for legal considerations—drive the development process of new AI assets for industry. However, given the lack of concrete standards and robust AI legislation, the gap between ethical principles and actionable approaches is still considerable. Different organisations have developed various methods based on multiple ethical principles to facilitate practitioners developing AI components worldwide. Nevertheless, these approaches can be driven by a self-claimed ethical shell or without a clear understanding of the impacts and risks involved in using their AI assets. The manufacturing sector has produced standards since 1990’s to guarantee, among others, the correct use of mechanical machinery, workers security, and environmental impact. However, a revision is needed to blend these with the needs associated with AI’s use. We propose using a vertical-domain framework for the manufacturing sector that will consider ethical perspectives, values, requirements, and well-known approaches related to risk management in the sector.
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Keywords
Responsible AI , Manufacturing , AI , Ethics , Framework , Risk management , Artificial intelligence
Citation
Vyhmeister, E., Gonzalez-Castane, G. and Östbergy, P.-O. (2023) ‘Risk as a driver for AI framework development on manufacturing’, AI and Ethics, 3(1), pp. 155–174. Available at: https://doi.org/10.1007/s43681-022-00159-3.
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https://hdl.handle.net/10468/15436
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Insight Centre for Data Analytics - Journal Articles
Computer Science - Journal Articles
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© The Authors 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.