Multi-stakeholder perspective on human-AI collaboration in industry 5.0

Thomas Hoch; Jorge Martinez-Gil; Mario Pichler; Agastya Silvina; Bernhard Heinzl; Bernhard Moser; Dimitris Eleftheriou; Hector Diego Estrada-Lugo; Maria Chiara Leva

doi:10.1007/978-3-031-46452-2_23

Multi-stakeholder perspective on human-AI collaboration in industry 5.0

Thomas Hoch, Jorge Martinez-Gil, Mario Pichler, Agastya Silvina, Bernhard Heinzl, Bernhard Moser, Dimitris Eleftheriou, Hector Diego Estrada-Lugo, Maria Chiara Leva

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.

Original language	English
Title of host publication	Artificial Intelligence in Manufacturing
Subtitle of host publication	Enabling Intelligent, Flexible and Cost-Effective Production Through AI
Publisher	Springer Nature
Pages	407-421
Number of pages	15
ISBN (Electronic)	9783031464522
ISBN (Print)	9783031464515
DOIs	https://doi.org/10.1007/978-3-031-46452-2_23
Publication status	Published - 8 Feb 2024

Keywords

AI-based software platforms
Human-AI teaming
Real-time monitoring
Smart manufacturing

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10.1007/978-3-031-46452-2_23Licence: CC BY

Cite this

Hoch, T., Martinez-Gil, J., Pichler, M., Silvina, A., Heinzl, B., Moser, B., Eleftheriou, D., Estrada-Lugo, H. D., & Leva, M. C. (2024). Multi-stakeholder perspective on human-AI collaboration in industry 5.0. In Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI (pp. 407-421). Springer Nature. https://doi.org/10.1007/978-3-031-46452-2_23

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abstract = "AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.",

keywords = "AI-based software platforms, Human-AI teaming, Real-time monitoring, Smart manufacturing",

author = "Thomas Hoch and Jorge Martinez-Gil and Mario Pichler and Agastya Silvina and Bernhard Heinzl and Bernhard Moser and Dimitris Eleftheriou and Estrada-Lugo, {Hector Diego} and Leva, {Maria Chiara}",

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doi = "10.1007/978-3-031-46452-2_23",

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Hoch, T, Martinez-Gil, J, Pichler, M, Silvina, A, Heinzl, B, Moser, B, Eleftheriou, D, Estrada-Lugo, HD & Leva, MC 2024, Multi-stakeholder perspective on human-AI collaboration in industry 5.0. in Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Springer Nature, pp. 407-421. https://doi.org/10.1007/978-3-031-46452-2_23

Multi-stakeholder perspective on human-AI collaboration in industry 5.0. / Hoch, Thomas; Martinez-Gil, Jorge; Pichler, Mario et al.
Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Springer Nature, 2024. p. 407-421.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Multi-stakeholder perspective on human-AI collaboration in industry 5.0

AU - Hoch, Thomas

AU - Martinez-Gil, Jorge

AU - Pichler, Mario

AU - Silvina, Agastya

AU - Heinzl, Bernhard

AU - Moser, Bernhard

AU - Eleftheriou, Dimitris

AU - Estrada-Lugo, Hector Diego

AU - Leva, Maria Chiara

PY - 2024/2/8

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N2 - AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.

AB - AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.

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KW - Human-AI teaming

KW - Real-time monitoring

KW - Smart manufacturing

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SN - 9783031464515

SP - 407

EP - 421

BT - Artificial Intelligence in Manufacturing

PB - Springer Nature

ER -

Multi-stakeholder perspective on human-AI collaboration in industry 5.0

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Keywords

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