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.

Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI The EU-funded XMANAI project focuses on Explainable AI, as the

Name: Aitor San Vicente Job title: General Manager Organization: UNIMETRIK Bio: Expert in advanced industrial metrology services, Aitor has extensive experience both in the field of advanced manufacturing processes, mainly in the die-cutting and stamping sector, and in the development of quality control and digitalization solutions and strategies.

In XMANAI we have set out to develop robust and insightful AI pipelines that can assist manufacturers in their everyday operations and decision-making processes. To achieve our goal, we are creating a collaborative environment in which the explainability of the ML models’ decisions lies at the heart of our AI pipelines design, development and roll out. Needless to say, in order for these AI pipelines to be properly configured, trained, evaluated, deployed and applied, constantly monitored, assessed and refined as needed, numerous other processes need to be in place.