@inproceedings{9babacd705614700a3ef1d7d4e2bb32a,
title = "Explainable AI in Manufacturing: an Analysis of Transparency and Interpretability Methods for the XMANAI Platform",
abstract = "The use of artificial intelligence (AI) in manufacturing has become increasingly common, but the lack of transparency and interpretability of AI models can limit their adoption in critical applications, due to lack of human understanding. Explainable AI (XAI) has emerged as a solution to this problem by providing insights into the decision-making process of AI models. This paper analyses different approaches for AI transparency and interpretability, starting from explainability by-design to post-hoc explainability, pointing out trade-offs, advantages and disadvantages of each one, as well as providing an overview of different applications in manufacturing processes. The paper concludes presenting XMANAI as an innovative platform for manufacturing users to develop insightful XAI pipelines that can assist in their everyday operations and decision-making. The comprehensive overview of methods here presented serves as the ground basis for the platform draft catalogue of XAI models.",
keywords = "AI, Explainability, Manufacturing, XAI",
author = "Rui Branco and Carlos Agostinho and Sergio Gusmeroli and Eleni Lavasa and Zoumpolia Dikopoulou and David Monzo and Fenareti Lampathaki",
note = "info:eu-repo/grantAgreement/EC/H2020/957362/EU# Publisher Copyright: {\textcopyright} 2023 IEEE.; 29th International Conference on Engineering, Technology, and Innovation, ICE 2023 ; Conference date: 19-06-2023 Through 22-06-2023",
year = "2023",
doi = "10.1109/ICE/ITMC58018.2023.10332373",
language = "English",
isbn = "979-8-3503-1518-9",
series = "IEEE International Conference on Engineering, Technology and Innovation",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "Proceedings of the 29th International Conference on Engineering, Technology, and Innovation",
address = "United States",
}
