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Explainable AI in Industry 4.0 and 5.0 This Special Issue aims to gather papers that focus on integrating and applying eXplainable AI (xAI) in the context of Industry 4.0 and 5.0. The themes that will be explored include the use of XAI in modern industrial settings and the collaboration between humans and machines in Industry 5.0, with a particular focus on trustworthiness, transparency, and accountability. As AI becomes more prevalent in industries, it is crucial to understand its nuances, particularly in terms of explainability. While there have been publications on the revolutionary nature of Industry 4.0 and 5.0, there has been little exploration of the critical role of XAI in these sectors. Previous special issues have covered AI and industrial revolutions, but a concerted effort to comprehend the importance of XAI in these settings is lacking. This Special Issue is intended for both academic researchers and industrial practitioners, with the potential to promote innovative collaborations.
Topics of interest include (but are not limited to):
-Data and information fusion in the industrial XAI context -Explainable systems fusing various sources of industrial information -Exploring XAI in the performance and efficiency of industrial systems -XAI for predictive maintenance -Forecasting of product and process quality -Explainable anomaly detection -Root Cause Analysis, Causal Reasoning -Automatic process optimization -Industrial process monitoring and modelling -Visual analytics and interactive machine learning -Remaining Useful Life -Decision-making assistance and resource optimization -Planning under uncertainty -Digital Twins for Predictive Maintenance -Analysis of usage patterns -AI transparency and accountability in smart factories -Ethical considerations in industrial deployment of AI -Industrial use cases for XAI (e.g., manufacturing, energy, transport) -Challenges and future directions for XAI in the industry
Guest editors:
Rita P. Ribeiro, PhD University Porto and INESC TEC, Porto, Portugal
João Gama, PhD University Porto and INESC TEC, Porto, Portugal
Slawomir Nowaczyk, PhD Halmstad University, Halmstad, Sweden
Sepideh Pashami, PhD Halmstad University, Halmstad, Sweden
Manuscript submission information:
The journal’s submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from January 29th, 2024. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: XAI in Industry 4.0 and 5.0” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Information Fusion | Journal | ScienceDirect.com by Elsevier.
Timeline:
Submission Open Date *29/01/2024
Final Manuscript Submission Deadline *29/07/2024
Editorial Acceptance Deadline *29/11/2024
Keywords:
Predictive Maintenance; Industry 4.0; Explainable AI; Machine Learning
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para appia [at] appia [ponto] pt
Explainable AI in Industry 4.0 and 5.0 This Special Issue aims to gather papers that focus on integrating and applying eXplainable AI (xAI) in the context of Industry 4.0 and 5.0. The themes that will be explored include the use of XAI in modern industrial settings and the collaboration between humans and machines in Industry 5.0, with a particular focus on trustworthiness, transparency, and accountability. As AI becomes more prevalent in industries, it is crucial to understand its nuances, particularly in terms of explainability. While there have been publications on the revolutionary nature of Industry 4.0 and 5.0, there has been little exploration of the critical role of XAI in these sectors. Previous special issues have covered AI and industrial revolutions, but a concerted effort to comprehend the importance of XAI in these settings is lacking. This Special Issue is intended for both academic researchers and industrial practitioners, with the potential to promote innovative collaborations.
Topics of interest include (but are not limited to):
-Data and information fusion in the industrial XAI context -Explainable systems fusing various sources of industrial information -Exploring XAI in the performance and efficiency of industrial systems -XAI for predictive maintenance -Forecasting of product and process quality -Explainable anomaly detection -Root Cause Analysis, Causal Reasoning -Automatic process optimization -Industrial process monitoring and modelling -Visual analytics and interactive machine learning -Remaining Useful Life -Decision-making assistance and resource optimization -Planning under uncertainty -Digital Twins for Predictive Maintenance -Analysis of usage patterns -AI transparency and accountability in smart factories -Ethical considerations in industrial deployment of AI -Industrial use cases for XAI (e.g., manufacturing, energy, transport) -Challenges and future directions for XAI in the industry
Guest editors:
Rita P. Ribeiro, PhD University Porto and INESC TEC, Porto, Portugal
João Gama, PhD University Porto and INESC TEC, Porto, Portugal
Slawomir Nowaczyk, PhD Halmstad University, Halmstad, Sweden
Sepideh Pashami, PhD Halmstad University, Halmstad, Sweden
Manuscript submission information:
The journal’s submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from January 29th, 2024. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: XAI in Industry 4.0 and 5.0” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Information Fusion | Journal | ScienceDirect.com by Elsevier.
Timeline:
Submission Open Date *29/01/2024
Final Manuscript Submission Deadline *29/07/2024
Editorial Acceptance Deadline *29/11/2024
Keywords:
Predictive Maintenance; Industry 4.0; Explainable AI; Machine Learning
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para appia [at] appia [ponto] pt