Compliance with principles and guidelines for ethical AI has a significant impact on companies engaged in the development of artificial intelligence (AI) systems. Specifically, ethics is a broad concept that continuously evolves over time and across cultural and geographical boundaries. International organisations (IOs), individual states, and private groups, all have an interest in defining the concept of ethics of AI. IOs, as well as regional and national bodies, have issued many decisions on AI ethics. Developing a system that complies with the ethical framework poses a complex challenge for companies, and the consequences of not complying with ethical principles can have severe consequences, making compliance with these requirements a key issue for companies. Furthermore, there is a shortage of technical tools to ensure that such AI systems comply with ethical criteria. The scarcity of ethics compliance checking tools for AI, and the current focus on defining ethical guidelines for AI development, has led us to undertake a proposal consisting in a semi-automated software model to verify the ethical compliance of an AI system’s code. To implement this model, we focus on the following important aspects: (1) a literature review to identify existing ethical compliance systems, (2) a review of principles and guidelines for ethical AI to determine the international and European views regarding AI ethics, and (3) the identification of commonly accepted principles and sub-principles of AI. These elements served to inform (4) our proposal for the design of a semi-automated software for verifying the ethical compliance of AI systems both at design-time (ethics-by-design perspective) and afterwards on the resulting software.

Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in the landscape of practical tools in the field of DMS and advances the state of the art. This system represents a complex process of integrated AI-powered technologies that creates an ontology, extracts information from documents, defines profiles, maps the extracted data in RDF format, and applies inference through a reasoning engine. The DMS was designed to help all those companies that manage their clients’ tax and administrative documents daily. Automation speeds up the management process so that companies can focus more on value-added services. The system was tested in a case study that focused on the preparation of tax returns. The results demonstrated the efficacy of the system in providing document management service.

Ontologies are essential tools for representing, organizing, and sharing knowledge across various domains. This study presents a methodology for ontology construction supported by large language models (LLMs), with an initial application in the automotive sector. Specifically, a user preference ontology for adaptive interfaces in autonomous machines was developed using ChatGPT-4o. Based on this case study, the results were generalized into a reusable methodology. The proposed workflow integrates classical ontology engineering methodologies with the generative and analytical capabilities of LLMs. Each phase follows well-established steps: domain definition, term elicitation, class hierarchy construction, property specification, formalization, population, and validation. A key innovation of this approach is the use of a guiding table that translates domain knowledge into structured prompts, ensuring consistency across iterative interactions with the LLM. Human experts play a continuous role throughout the process, refining definitions, resolving ambiguities, and validating outputs. The ontology was evaluated in terms of logical consistency, structural properties, semantic accuracy, and inferential completeness, confirming its correctness and coherence. Additional validation through SPARQL queries demonstrated its reasoning capabilities. This methodology is generalizable to other domains, if domain experts adapt the guiding table to the specific context. Despite the support provided by LLMs, domain expertise remains essential to guarantee conceptual rigor and practical relevance.

The increasing complexity in hospital waste management requires innovative solutions that integrate sustainability and regulatory compliance. This study proposes an AI-based decision tool to support the circular management of healthcare waste. The approach combines two key elements: (i) the systematic qualitative analysis of international, European, and national regulations, scientific literature, and best practices aimed at identifying strategic actions; (ii) the prioritization of these actions through machine learning, using a Random Forest classifier. We identified 55 actions, grouped into 13 thematic areas, and used them as input variables to assess their impact on regulatory compliance. The variable importance analysis allowed us to classify actions according to their strategic relevance, guiding the structure of the tool and its user interface. Validation, conducted on four simulated case studies, demonstrated the system’s ability to improve compliance monitoring, operational efficiency, and the implementation of circular economy and Zero-Waste strategies. The proposed model represents a scalable and evidence-based solution capable of supporting the ecological transition of healthcare facilities in line with EU directives and the Sustainable Development Goals.

This study examines the effectiveness of ICT training programmes for academics at selected universities in Africa, specifically in Cameroon, Nigeria, and South Africa. Using a qualitative approach, we conducted semi-structured interviews with five ICT trainers and three trainees to better understand their experiences. The findings highlight both the challenges and the strengths that could be used to improve ICT training and make it more effective and relevant to the participants. Four key themes emerge from our analysis: course objectives and content, teaching strategies, implementation challenges, and expectations/impact of the courses. The objectives of the courses vary considerably—some focus on the practical application of ICT, while others aim to improve the participants’ understanding of ICT systems. Teaching methods also differ, ranging from more traditional teaching approaches to collaborative, project-oriented methods. However, we identify significant challenges, particularly poor internet connectivity and a lack of sufficient technological resources, which affect the learning process. In addition, there is often a divergence between trainer expectations and trainee perceptions of the impact of these courses on their professional development. This study aims to contribute to the definition of strategies to improve the effectiveness of ICT training, in the African context, including Cameroon, Nigeria and South Africa.