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Lessons learn on re...
Lessons learn on responsible AI implementation : the ASSISTANT use case
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- Vyhmeister, Eduardo (author)
- Insight Centre of Data Analytics, University College Cork, Cork, Ireland
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- Castane, Gabriel G. (author)
- Insight Centre of Data Analytics, University College Cork, Cork, Ireland
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- 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.
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In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022. - : Elsevier. ; , s. 377-382
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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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