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Lessons learn on responsible AI implementation : the ASSISTANT use case

Vyhmeister, Eduardo (author)
Insight Centre of Data Analytics, University College Cork, Cork, Ireland
Castane, Gabriel G. (author)
Insight Centre of Data Analytics, University College Cork, Cork, Ireland
Buchholz, Johan (author)
Universidad Europea Vidrina, Frankfurt, Germany
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Östberg, Per-Olov (author)
Umeå universitet,BiTi Innovations, Umeå, Sweden
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 (creator_code:org_t)
Elsevier, 2022
2022
English.
In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022. - : Elsevier. ; , s. 377-382
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • Currently, pioneer companies are working hard to construct applied ethical frameworks in different sectors for using AI components that generate trust in their clients and workforce. However, independent of these few companies, there is still a considerable gap between understanding the impact of using responsible AI components, the implications of the lack of use, and what is currently applied in the industrial sector. Given that industry has shown an increased commitment to incorporating AI components, works focus on broadening the understanding of manufacturing sector stakeholders of what approaches could be considered within AI life-cycle, reducing the gap between principles and actionable requirements, and defining fundamental considerations based on risk management for incorporating, and managing, AI-based on responsible AI are required. In this work, we present a summary of the most suitable approaches that can be used for implementation and the lessons learned from a European Funded project (ASSISTANT).

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)

Keyword

AI
AI en manufacturing
AI ethics
design methodology for HMS
Human centered automation
responsible AI
standardisation

Publication and Content Type

ref (subject category)
kon (subject category)

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