Ethics considerations for manufacturing XAI: the XMANAI Ethical Evaluation Framework
The ethics implications of AI and machine learning are becoming increasingly relevant due to their implementations across several sectors. This is the case also for their application to the manufacturing domain, which is still a quite new application of AI technology.
Partner Spotlight – Ford Motor Company
Name: Javier Colomer Barberá
Job title: New Technologies Engineer
Organization: Ford Motor Company
Bio: Javier Colomer is a Telecommunications Engineer from the Polytechnic University of Valencia.
XAI Model Guard: The XMANAI AI Models Security Framework
As manufacturing organizations are embracing the Industry 4.0 initiative that is revolutionizing the manufacturing sector towards the realization of smart factories, the adoption rate of technologies related to Artificial Intelligence (AI), machine learning, and analytics is also growing.
Explainable AI: a key to trust and acceptance of AI-based decision support systems
Explainable AI: a key to trust and acceptance of AI-based decision support systems Artificial intelligence is often based on complex algorithms and mathematical models that are difficult to understand. One of the characteristics of modern AI, based mainly on neural networks, is that it uses ‘black-box’ models, i.e. ‘boxes’ that make decisions without the user […]
Partner Spotlight – CNH Industrial
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.
Industrial Asset Graph Modelling in XMANAI
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).
XMANAI Validation Environment for AI Models
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.
zExplAIn, Improving Manufacturing Processes with Explainable AI
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.
XMANAI partner spotlight – Deep Blue
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
Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI The EU-funded XMANAI project focuses on Explainable AI, as the ability to make machine decision-making processes understandable; Explainability has proven to be a key element in stimulating the adoption of AI in various areas because it provides transparent and understandable information about algorithmic […]
XMANAI partner spotlight – UNIMETRIK
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.
AI Algorithms Lifecycle Management and Collaboration
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.
XMANAI partner spotlight – Knowledgebiz
Name: Carlos Agostinho
Job title: Dissemination Manager and Company Responsible
Organization: Knowledgebiz, Lda
Bio: Carlos Agostinho is a senior researcher at UNINOVA (www.uninova.pt) and a digital transformation expert.
XMANAI partner spotlight – UBITECH
Name: Konstantinos Perakis
Job title: Head of Data Science and Analytics R&D Unit
Organization: UBITECH
Bio: Dr. Konstantinos Perakis was born in Athens, Greece in 1979. He received his diploma in Electrical & Computer Engineering from the National Technical University of Athens in October 2003.
A First Glimpse into the XMANAI platform
The first official release of the XMANAI Platform, namely Alpha Version, has been successfully delivered for early assessment by the XMANAI stakeholders on July 2022 (M21) as planned.
