Cyber-human systems are formed by the coordinated interaction of human and computational components. The latter are justified to the extent that they are meaningful to humans – in both senses of ‘meaning’, i.e., in the sense of semantics as well as in the sense of purpose or significance. On one hand, the data these components manipulate only acquire meaning when mapped to shared human conceptualizations of the world. On the other hand, they can only be justified if ethically designed. Cyber-human systems are trustworthy if the interoperation of their components is meaning preserving, i.e., if we can semantically interoperate these components; and transparently demonstrate (i.e., explain) how their interoperation positively contributes to human values and goals. In this talk, I will present a notion of explanation termed Ontological Unpacking, which aims at explaining symbolic domain descriptions (conceptual models, knowledge graphs, logical specifications). I show that it is this explanatory nature that is required for semantic interoperability and, hence, trustworthiness. Finally, I will argue that the current trend in XAI (Explainable AI) in which “to explain is to produce a symbolic artifact” is an incomplete project as these artifacts are not “inherently interpretable”, and that they should be taken as the beginning of the road to explanation, not the end.
Giancarlo Guizzardi is a Full Professor of Software Science and Evolution as well as Chair and Department Head of Semantics, Cybersecurity & Services (SCS) at the University of Twente, The Netherlands. He has also been a Guest Professor at Stockholm University (Sweden), the Technical University of Vienna (Austria), and the University of Trento (Italy). He has been active for nearly three decades in the areas of Formal and Applied Ontology, Conceptual Modeling, Enterprise Computing and Information Systems Engineering, working with a multidisciplinary approach in Computer Science that aggregates results from Philosophy, Cognitive Science, Logics and Linguistics. He is the main contributor to the Unified Foundational Ontology (UFO) and to the OntoUML modeling language. Over the years, he has delivered keynote speeches in several key international conferences in these fields (e.g., ER, CAiSE, BPM, IEEE ICSC). He is currently an associate editor of a number of journals including Applied Ontology and Data & Knowledge Engineering, a co-editor of the Lecture Notes in Business Information Processing series, and a member of several international journal editorial boards. He is currently the Chair of the Steering Committee of the International Conference on Conceptual Modeling (ER), a member of the Steering Committees of CAiSE, EDOC, and IEEE CBI, and a member of the Advisory Board of the International Association for Ontology and its Applications (IAOA). Finally, he is an ER fellow.
This conference examines the dynamics of collaborative innovation for the development of ethical and human-centered Artificial Intelligence (AI)-based information systems through European consortia. In the context of the increasing complexity of the information systems around us and intensified competition in the generation of innovative ideas, collaboration around distinctive European values and approaches emerges as a key catalyst for achieving significant advances in AI, overcoming obstacles and cultivating a high-impact innovation ecosystem for societal transformation. Strategies to strengthen synergistic cooperation between academia, industry and the public sector will be discussed, with a particular focus on flagship initiatives such as the European Network of AI Centers of Excellence and AI Factories. The discussion will focus on how these collaborations facilitate access to critical resources, foster efficient knowledge transfer, and promote agile adoption and development of AI technologies. In parallel, the conference will address the ethical and regulatory challenges inherent in collaborative AI innovation. It will explore regulatory frameworks that encourage the development of responsible and trustworthy AI, without compromising creative and innovative potential. In addition, it will examine open innovation models designed to boost European competitiveness in the global AI arena, balancing the need for technological advancement with ethical and social imperatives..
Rebeca Gutierrez is an Innovation Service Delivery Manager at Ayesa, leading the production of the company's Innovation Department. She oversees the comprehensive management of technology-driven research and development projects in the fields of renewable energy, mobility, and climate, funded by European programs such as H2020, Horizon Europe, and Digital Europe. She manages a multidisciplinary team of over 15 professionals and a project portfolio exceeding 10 million euros. She has previously held roles as PMO Manager for ENEL X’s Data Science Department across Italy, Spain, and the USA, leading global project management and financial oversight. Before that, she served as PMO Manager at MI in Madrid, where she was responsible for the integrated management of technology, product delivery, and support teams. She also worked as a Project Manager at Banco Santander in Madrid and London, handling large-scale financial technology projects, and as a Technical Analyst at SENER in Vizcaya. In addition to her industry experience, she is actively involved in academia and research. She is a Lecturer in the Master’s in Software Engineering at the University of Seville, where she teaches software analysis and design, project management, and software quality. She is also a Patron at FIDETIA, a foundation dedicated to fostering research and technology transfer in artificial intelligence and AI ethics. Since March 2024, she has served as an independent evaluator for Spain’s State Agency for Innovation, assessing "Knowledge Generation" projects. She is currently pursuing a PhD at the University of Seville (Group ES3) and holds a Bachelor's degree in Computer Systems from the University of Wales.
There’s too much code in the world and it’s growing at staggering exponential rates. No approach in software engineering or programming paradigm has made a dent in this trend. What should we do? Buy a low code platform? Will AI bring us salvation? The key to successful software engineering is maintaining domain knowledge and architectural knowledge, the “what” and “how”. Why not maintain them as code? I argue that the in-house development of domain-specific languages (DSLs) modeling different aspects of a software system offer a potential way out. State-of-the art language workbenches make this approach feasible, which I demonstrate with a functional low code platform, developed in under 1000 lines of code. Language engineering for all!
Tijs van de Storm is a senior researcher in the Software Analysis and Transformation (SWAT) group at Centrum Wiskunde & Informatica (CWI) in Amsterdam, and full professor in Software Engineering at the University of Groningen in Groningen. The main research question that guides his work is: how to make better programming languages, and how to better make programming languages, with the goal of improving software engineering through new and better software languages, and developing the tools and techniques to engineer them.