Explainability as the key ingredient for AI adoption in Industry 5.0 settings
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/164433 |
Resumo: | Funding Information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research leading to this work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no: 957362. Publisher Copyright: Copyright © 2023 Agostinho, Dikopoulou, Lavasa, Perakis, Pitsios, Branco, Reji, Hetterich, Biliri, Lampathaki, Rodríguez Del Rey and Gkolemis. |
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Explainability as the key ingredient for AI adoption in Industry 5.0 settingsbusiness valuedecision-makingexplainable AIFuzzy Cognitive Mapsmanufacturing industryXMANAI platformArtificial IntelligenceSDG 9 - Industry, Innovation, and InfrastructureFunding Information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research leading to this work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no: 957362. Publisher Copyright: Copyright © 2023 Agostinho, Dikopoulou, Lavasa, Perakis, Pitsios, Branco, Reji, Hetterich, Biliri, Lampathaki, Rodríguez Del Rey and Gkolemis.Explainable Artificial Intelligence (XAI) has gained significant attention as a means to address the transparency and interpretability challenges posed by black box AI models. In the context of the manufacturing industry, where complex problems and decision-making processes are widespread, the XMANAI platform emerges as a solution to enable transparent and trustworthy collaboration between humans and machines. By leveraging advancements in XAI and catering the prompt collaboration between data scientists and domain experts, the platform enables the construction of interpretable AI models that offer high transparency without compromising performance. This paper introduces the approach to building the XMANAI platform and highlights its potential to resolve the “transparency paradox” of AI. The platform not only addresses technical challenges related to transparency but also caters to the specific needs of the manufacturing industry, including lifecycle management, security, and trusted sharing of AI assets. The paper provides an overview of the XMANAI platform main functionalities, addressing the challenges faced during the development and presenting the evaluation framework to measure the performance of the delivered XAI solutions. It also demonstrates the benefits of the XMANAI approach in achieving transparency in manufacturing decision-making, fostering trust and collaboration between humans and machines, improving operational efficiency, and optimizing business value.UNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasRUNAgostinho, CarlosDikopoulou, ZoumpoliaLavasa, EleniPerakis, KonstantinosPitsios, StamatisBranco, RuiReji, SangeethaHetterich, JonasBiliri, EvmorfiaLampathaki, FenaretiRodríguez Del Rey, SilviaGkolemis, Vasileios2024-03-05T00:16:09Z2023-12-112023-12-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/164433eng2624-8212PURE: 84377962https://doi.org/10.3389/frai.2023.1264372info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:52:12Zoai:run.unl.pt:10362/164433Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:00:10.353222Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
title |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
spellingShingle |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings Agostinho, Carlos business value decision-making explainable AI Fuzzy Cognitive Maps manufacturing industry XMANAI platform Artificial Intelligence SDG 9 - Industry, Innovation, and Infrastructure |
title_short |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
title_full |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
title_fullStr |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
title_full_unstemmed |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
title_sort |
Explainability as the key ingredient for AI adoption in Industry 5.0 settings |
author |
Agostinho, Carlos |
author_facet |
Agostinho, Carlos Dikopoulou, Zoumpolia Lavasa, Eleni Perakis, Konstantinos Pitsios, Stamatis Branco, Rui Reji, Sangeetha Hetterich, Jonas Biliri, Evmorfia Lampathaki, Fenareti Rodríguez Del Rey, Silvia Gkolemis, Vasileios |
author_role |
author |
author2 |
Dikopoulou, Zoumpolia Lavasa, Eleni Perakis, Konstantinos Pitsios, Stamatis Branco, Rui Reji, Sangeetha Hetterich, Jonas Biliri, Evmorfia Lampathaki, Fenareti Rodríguez Del Rey, Silvia Gkolemis, Vasileios |
author2_role |
author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias CTS - Centro de Tecnologia e Sistemas RUN |
dc.contributor.author.fl_str_mv |
Agostinho, Carlos Dikopoulou, Zoumpolia Lavasa, Eleni Perakis, Konstantinos Pitsios, Stamatis Branco, Rui Reji, Sangeetha Hetterich, Jonas Biliri, Evmorfia Lampathaki, Fenareti Rodríguez Del Rey, Silvia Gkolemis, Vasileios |
dc.subject.por.fl_str_mv |
business value decision-making explainable AI Fuzzy Cognitive Maps manufacturing industry XMANAI platform Artificial Intelligence SDG 9 - Industry, Innovation, and Infrastructure |
topic |
business value decision-making explainable AI Fuzzy Cognitive Maps manufacturing industry XMANAI platform Artificial Intelligence SDG 9 - Industry, Innovation, and Infrastructure |
description |
Funding Information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research leading to this work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no: 957362. Publisher Copyright: Copyright © 2023 Agostinho, Dikopoulou, Lavasa, Perakis, Pitsios, Branco, Reji, Hetterich, Biliri, Lampathaki, Rodríguez Del Rey and Gkolemis. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-11 2023-12-11T00:00:00Z 2024-03-05T00:16:09Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/164433 |
url |
http://hdl.handle.net/10362/164433 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2624-8212 PURE: 84377962 https://doi.org/10.3389/frai.2023.1264372 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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21 application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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