Ahangar, M.N. orcid.org/0009-0006-0764-4847, Farhat, Z.A. orcid.org/0000-0002-0687-2817 and Sivanathan, A. orcid.org/0000-0002-4389-9488 (2025) AI trustworthiness in manufacturing: challenges, toolkits, and the path to Industry 5.0. Sensors, 25 (14). 4357. ISSN: 1424-8220
Abstract
The integration of Artificial Intelligence (AI) into manufacturing is transforming the industry by advancing predictive maintenance, quality control, and supply chain optimisation, while also driving the shift from Industry 4.0 towards a more human-centric and sustainable vision. This emerging paradigm, known as Industry 5.0, emphasises resilience, ethical innovation, and the symbiosis between humans and intelligent systems, with AI playing a central enabling role. However, challenges such as the “black box” nature of AI models, data biases, ethical concerns, and the lack of robust frameworks for trustworthiness hinder its widespread adoption. This paper provides a comprehensive survey of AI trustworthiness in the manufacturing industry, examining the evolution of industrial paradigms, identifying key barriers to AI adoption, and examining principles such as transparency, fairness, robustness, and accountability. It offers a detailed summary of existing toolkits and methodologies for explainability, bias mitigation, and robustness, which are essential for fostering trust in AI systems. Additionally, this paper examines challenges throughout the AI pipeline, from data collection to model deployment, and concludes with recommendations and research questions aimed at addressing these issues. By offering actionable insights, this study aims to guide researchers, practitioners, and policymakers in developing ethical and reliable AI systems that align with the principles of Industry 5.0, ensuring both technological advancement and societal value.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | Artificial Intelligence (AI); manufacturing; Industry 4.0; Industry 5.0; AI trustworthiness; transparency; fairness; robustness; accountability; ethical AI; bias mitigation; explainability; AI Toolkits; sustainable manufacturing; human-centric AI |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > University of Sheffield Research Centres and Institutes > AMRC with Boeing (Sheffield) The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > AMRC with Boeing (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Jul 2025 10:47 |
Last Modified: | 23 Jul 2025 10:47 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/s25144357 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229564 |