Explainable AI: a key to trust and acceptance of AI-based decision support systems Artificial intelligence is often based on complex algorithms and

Name: Claudia Campanella Job title: Manager of Ergonomics-HMI-VR-AR Organization: CNH Industrial Bio: Claudia Campanella graduated in Industrial Design at the Polytechnic of Turin, realizing a thesis in physical ergonomics. She started working at Fiat Auto in 2000 and at the same time, she attended the Master in Ergonomics in which she created a thesis in cognitive ergonomics.

In the Industrial sector specifically, graph networks can describe pathways of IoT devices and sensor networks (Aggarwal, et al., 2017) in the framework of predictive maintenance, or represent associations between resources, daily workload and production in decision-making and dynamic scheduling problems (Hu, et al., 2020).

The Explainable AI (XAI) in Manufacturing (XMANAI) project aims to provide a framework for the development and deployment of AI models in the manufacturing industry.

In the new era of Industry 4.0, AI systems are becoming increasingly prevalent and cost-effective. With the ability to analyze vast amounts of data, factories can reduce expenses, boost productivity, and minimize waste.

Name: Linda Napoletano Job title: Head of the Manufacturing Area Organization: Deep Blue srl Bio: Linda Napoletano holds a Ph.D. in Human-Computer Interaction. Since 2002, she has been working on EU-co-funded projects aiming at designing and validating humans integration and interactions into highly innovative processes.