The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.

The adoption of AI in manufacturing enables numerous benefits that can significantly impact productivity, efficiency, and decision-making processes. AI algorithms can optimize production schedules, inventory management, and supply chain operations by analyzing historical data and producing demand forecasts. In spite of these benefits, some challenges such as integration, lack of data infrastructure and expertise, and resistance to change need to be addressed for the industry to successfully adopt AI. To overcome these issues, we introduce the AI Model Generation framework (AMG), able to automatically generate AI models that adjust to the user’s needs. More precisely, the model development process involves the execution of a whole chain of sub-processes, including data loading, automated data pre-processing, cost computation, automatic model hyperparameter tuning, training, inference, explainability generation, standardization, and containerization. We expect our approach to aid non-expert users into more effectively producing machine and deep learning algorithms and hyperparameter settings that are appropriate to solve their problems without sacrificing privacy and relying on third-party services and infrastructure as few as possible. Keywords AutoML Explainability (XAI) Hyperparameter tuning Pre-processing Standardization Model deployment

Digital research infrastructures underpin data-intensive science, reproducible experimentation, and collaborative innovation. In Africa, their development remains hindered by fragmentation, limited computing and connectivity resources, shortages of technical expertise, and sustainability constraints. This paper presents the vision, methodology, and deployment roadmap of DIGITAfrica, a Horizon Europe initiative preparing a pan-African Research Infrastructure in digital sciences. Building on a multi-country demand analysis and lessons from the ESFRI research infrastructures SLICES and SoBigData, we introduce a blueprint-based methodology for specifying interoperable, incrementally deployable, and sustainable digital infrastructures. We describe two representative blueprints—edge-interoperable AI/ML and heterogeneous networking—and the shared service architecture that operationalise them. Finally, we outline the stakeholder engagement process intended to support adoption, capacity building, and long-term governance. Overall, DIGITAfrica proposes a federated framework to strengthen digital science capabilities in Africa through shared infrastructure, reusable experimental environments, and coordinated community development.