Thematic Session 14

THEMATIC SESSION #14

Davide Calvaresi

University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis)

• davide.calvaresi@hevs.ch

Francisco Fernández de Vega

University of Extremadura

• fcofdez@unex.es

Simona Tiribelli

University of Macerata, MIT

• simona.tiribelli@unimc.it

Elia Pacioni

University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), University of Extremadura

• elia.pacioni@hevs.ch

This special session welcomes works exploring the intersection of distributed (e.g., federated) learning, hybrid AI models, and the ethical and societal challenges of emerging distributed technologies. As privacy-preserving and decentralized AI frameworks gain traction, the ability to select and shape high-quality data and define effective strategies for guiding the learning process is impelling.

Moreover, the role of human expertise in shaping AI models (through data selection, labeling, or oversight) impacts performance and fairness significantly. An emerging perspective in this space is Machine Teaching (MT), addressing human guidance to optimize AI learning strategies (rather than solely relying on passive data collection). While still a developing field, MT has the potential to enhance teacher-aware AI systems, improving transparency and interpretability.

Finally, hybrid AI approaches, combining symbolic reasoning with data-driven methods, promise to overcome the limitations of standalone models by enabling robust, interpretable, and adaptive solutions. However, these advancements require a critical focus on the ethical (e.g., fairness and societal impact) challenges introduced by algorithmic-based decisions.

The session will bring together researchers and practitioners to discuss novel methodologies, real-world applications, and frameworks that ensure responsible development and deployment of AI technologies. Topics include distributed learning, teacher-aware AI systems, integration of hybrid models, and ethical frameworks for transparency, fairness, and accountability in AI systems. The session seeks to foster interdisciplinary collaboration and shape the future of equitable, effective, and ethical AI systems by addressing these pressing challenges.

Prof. Dr.Davide Calvaresi is an Associate Professor at the School of Engineering (HEI) at University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis) since mid-2024.
Previously, Davide has been a senior researcher for the AISLab group in HES-SO Valais-Wallis (2018-2024), after having gotten his Ph.D. in Emerging Digital Technologies - Real-Time Embedded Systems from Sant'Anna School of Advanced Studies, Italy, in (2014-2018).
Davide's research is focused on, but not limited to, real-time multi-agent systems, explainable artificial intelligence, agent-based simulations, distributed learning, blockchain, and assistive/rehabilitative technologies.
He has been chair of several workshops including RTcMAS2018, BCT4MAS (2018-2020), and EXTRAAMAS (2019-2025). Moreover, he has been the primary investigator of the SEAMLESS project, aiming at enforcing timing compliance in MAS, technical PI of the European project EXPECTATION, aiming at bridging sub-symbolic and symbolic AI to foster interpretability and explainability in Multi-Agent Systems, and taken part in several other (inter)national projects.

Francisco Fernández de Vega is a Full Professor of Computer Architecture at the University of Extremadura and Director of the GEA Group. He received his PhD from the University of Extremadura and was awarded the PhD Extraordinary Award in 2002.
His research lies at the intersection of Parallel and Distributed Computing, Artificial Intelligence, and Computational Creativity, with applications in Arts and Music.
He has organized numerous international workshops and special sessions at leading conferences such as ACM GECCO, IEEE ICPP, IEEE CEC, IEEE WCCI, and IEEE PACT. He has also served as Local Chair of EvoApps 2020 and co-editor of the conference for eight years.
Francisco Fernández de Vega and his PhD students have received multiple international awards, including PPSN 2002, EvoHot 2008, and the ACM GECCO Evolutionary Art Competition Award in 2013. His artworks were also internationally selected at the Show Your World Competition 2017 in New York. His project, Sharpmony, won the Best AI App Award from the Spanish Association of Artificial Intelligence in 2021.
He was CETA-CIEMAT Chair from 2007-2009, University of Extremadura CIO from 2005-2007, and Vice-Dean of Research at Centro Universitario de Mérida from 2004-2005.
He is an IEEE Senior Member and Associate Editor of AI Communications.

Simona Tiribelli is the Director for AI Ethics at the Institute for Technology & Global Health (Boston, US), AI policy research and innovation center of the PathCheck Foundation, non-profit founded at the Massachusetts Institute of Technology. She is also tenure-track assistant professor in ethics at the University of Macerata, where she teaches ethics of AI and global justice and technology, and is key person in the Jean Monnet Chair Ethics for Inclusive Digital Europe, funded by the European Commission. With a European PhD cum laude in AI ethics, she was awarded the prestigious Fulbright Scholarship in 2019, appointed as Visiting Fellow in AI Ethics at the MIT Media Lab in 2020, and as Visiting Scholar at New York University in 2023. Since 2017, she has been a speaker at over 70 conferences in Italy, the US, and Canada (Harvard University, MIT, Tufts University, Toronto University, TUM, etc.), authored +40 articles in leading international scientific journals, and published two books.

Elia Pacioni is a PhD student at the University of Extremadura and a research assistant at the University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis).
He holds a Bachelor’s degree in Computer and Automation Engineering from the Università Politecnica delle Marche (Ancona, Italy) and two Master’s degrees from the University of Extremadura (Mérida, Spain): one in Research Engineering and Architecture, specializing in Information and Communications Technology, and another in Technology Innovation Management.
His research interests include evolutionary algorithms, federated learning, and computer vision.