A responsible AI framework: pipeline contextualisation

dc.contributor.authorVyhmeister, Eduardoen
dc.contributor.authorCastane, Gabrielen
dc.contributor.authorÖstberg, P.-O.en
dc.contributor.authorThevenin, Simonen
dc.date.accessioned2024-01-26T10:31:34Z
dc.date.available2024-01-26T10:31:34Z
dc.date.issued2022-04-19en
dc.description.abstractIncorporating ethics and values within the life cycle of an AI asset means securing its development, deployment, use, and decommission under these perspectives. These approaches depend on the market domain where AI is operational – considering the interaction and the impact on humans if any process does not perform as expected – and the legal compliance, both required to ensure adequate fulfilment of ethics and values. Specifically, in the manufacturing sector, standards were developed since the 1990’s to guarantee, among others, the correct use of mechanical machinery, systems robustness, low product variability, workers safety, system security, and adequate implementation of system constraints. However, it is challenging to blend the existing practices with the needs associated with deployments of AI in a trustworthy manner. This document provides an extended framework for AI Management within the Manufacturing sector. The framework is based on different perspectives related to responsible AI that handle trustworthy issues as risk. The approach is based on the idea that ethical considerations can and should be handled as hazards. If these requirements or constraints are not adequately fulfilled and managed, it is expected severe negative impact on different sustainable pillars. We are proposing a well-structured approach based on risk management that would allow implementing ethical concerns in any life cycle stages of AI components in the manufacturing sector. The framework follows a pipeline structure, with the possibility of being extended and connected with other industrial Risk Management Processes, facilitating its implementation in the manufacturing domain. Furthermore, given the dynamic condition of the regulatory state of AI, the framework allows extension and considerations that could be developed in the future.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationVyhmeister, E., Castane, G., Östberg, P.-O. and Thevenin, S. (2023) ‘A responsible AI framework: pipeline contextualisation’, AI and Ethics, 3(1), pp. 175–197. Available at: https://doi.org/10.1007/s43681-022-00154-8en
dc.identifier.doihttps://doi.org/10.1007/s43681-022-00154-8en
dc.identifier.endpage197en
dc.identifier.issn2730-5953en
dc.identifier.issn2730-5961en
dc.identifier.issued1en
dc.identifier.journaltitleAI and Ethicsen
dc.identifier.startpage175en
dc.identifier.urihttps://hdl.handle.net/10468/15435
dc.identifier.volume3en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofAI and Ethicsen
dc.rights© 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/.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectResponsible AIen
dc.subjectManufacturingen
dc.subjectAIen
dc.subjectEthicsen
dc.subjectFrameworken
dc.titleA responsible AI framework: pipeline contextualisationen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
oaire.citation.issue1en
oaire.citation.volume3en
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