AUTHOR=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 TITLE=Explainability as the key ingredient for AI adoption in Industry 5.0 settings JOURNAL=Frontiers in Artificial Intelligence VOLUME=6 YEAR=2023 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1264372 DOI=10.3389/frai.2023.1264372 ISSN=2624-8212 ABSTRACT=

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.