Publications
The following is a list of the publications produced as part of the AI4Gov project. These works highlight the consortium’s research and contributions across a range of topics in artificial intelligence, machine learning, explainability, and trustworthy data-driven governance. The list showcases efforts to advance the understanding and application of AI in both academic and practical settings, supporting the project’s commitment to responsible and innovative technological development.
- Manias, G., Apostolopoulos, D., Athanassopoulos, S., Borotis, S., Chatzimallis, C., Chatzipantelis, T., Karabet, A. (2023). AI4Gov: Trusted AI for Transparent Public Governance Fostering Democratic Values. Proceedings of the 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) (pp. 548-555). Pafos: IEEE. doi:10.1109/DCOSS-IoT58021.2023.00090
- Mavrogiorgos, K., Kiourtis, A., Mavrogiorgou, A., Manias, G., & Kyriazis, D. (2024). A Question Answering Software for Assessing AI Policies of OECD Countries. ESSE '23: Proceedings of the 4th European Symposium on Software Engineering, (pp. 31-36). doi:10.1145/3651640.3651651
- Fournier, F., Limonad, L., Skarbovsky, I., & David, Y. (2025). The WHY in Business Processes: Discovery of Causal Execution Dependencies. Künstliche Intelligenz, 39, 197-219. doi:10.1007/s13218-024-00883-4
- Fahland, D., Fournier, F., Limonad, L., Skarbovsky, I., & Swevels, A. J. (2025). How well can a large language model explain business processes as perceived by users? Data & Knowledge Engineering, 157(102416). doi:10.1016/j.datak.2025.102416
- (Bassan, et al., 2026)Guček, A., Kovačič, M., & Draksler, T. Z. (2024). Bridging Global Disparities: An Analytics Pipeline for Detecting Bias and Incompleteness in Rare Diseases Datasets (Poster). Meeting abstracts from the 12th European Conference on Rare Diseases and Orphan Products. Retrieved from
- Manias, G., Borotis, S., Chatzimallis, C., Draksler, T. Z., Gucek, A., Fournier, F., . . . Papa, X. S. (2024). Fostering Fundamental Human Rights and Trustworthiness though the Utilization of Emerging Technologies: the AI4Gov Platform. Proceedings from the 2024 Global Conference onAI and Human Rights. Ljubljana. Retrieved from https://www.ai-right-to-life.si/en/_files/ugd/510aed_3d76b33174f243e2b9f78067a3712437.pdf
- Mavrogiorgos, K., Kiourtis, A., Mavrogiorgou, A., Gucek, A., Menychtas, A., & Kyriazis, D. (n.d.). Mitigating Bias in Time Series Forecasting for Efficient Wastewater Management. 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE. doi:10.1109/ICICoS62600.2024.10636931
- Mavrogiorgos, K., Kiourtis, A., Mavrogiorgou, A., Menychtas, A., & Kyriazis, D. (2024). Bias in Machine Learning: A Literature Review. Applied Sciences, 14(19), 8860. doi:10.3390/app14198860
- Fournier, F., Limonad, L., & Skarbovsky, I. (2024). Towards a Benchmark for Causal Business Process Reasoning with LLMs. International Conference on Business Process Management, (pp. 233-246). doi:10.1007/978-3-031-78666-2_18
- Amit, G., & Gur, S. (2024). eXplainable Random Forest. Proceedings of Workshop on Embracing Human-Aware AI in Industry 5.0, European Conference on Artificial Intelligence (ECAI). Retrieved from https://ceur-ws.org/Vol-3765/Camera_Ready_Paper-10.pdf
- Bassan, S., Eliav, R., & Gur, S. (2025). EXPLAIN YOURSELF, BRIEFLY! SELF-EXPLAINING NEURAL NETWORKS WITH CONCISE SUFFICIENT REASONS. The Thirteenth International Conference on Learning Representations (ICLR). Retrieved from https://arxiv.org/pdf/2502.03391
- Manias, G., Agapitou, C., Borovits, N., Guček, A., Karabetian, A., Kovacic, M., . . . Kyriazis, D. (n.d.). Multilingual Classification of AI-Oriented Policy Documents based on Bias Types. Data for Policy 2025 (DfP’25) - Europe Book of Abstracts, (pp. 132-133). https://zenodo.org/records/15675928
- Limonad, L., Fournier, F., Hadar Mulian, Manias, G., Spiros Borotis, & Danai Kyrkou. (n.d.). Selecting the Right LLM for eGov Explanations. Eleventh International Conference on eDemocracy & eGovernment (ICEDEG). IEEE. doi:10.1109/ICEDEG65568.2025.11081620
- David, Y., Fournier, F., Limonad, L., & Skarbovsky, I. (2025). The WHY in Business Processes: Unification of Causal Process Models. BPM Forum in BPM Conference, (pp. 40-57). doi:10.1007/978-3-032-02929-4_3
- Mavrogiorgos, K., Gur, S., Kalantzis, N., Tzelaptsis, K., Papageorgiou, X. S., & Karabetian, A. (2025). Combining Explainable Artificial Intelligence (XAI) With Blockchain Towards Trustworthy Data-Driven Policies. 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) (pp. 1042-1049). IEEE. doi:10.1109/DCOSS-IoT65416.2025.00157
- Mavrogiorgos, K., Kiourtis, A., Mavrogiorgou, A., Apostolopoulos, D., Menychtas, A., & Kyriazis, D. (2025). Proceedings of the 11th International Conference on Time Series and Forecasting, 11. doi:10.3390/cmsf2025011004
- Bassan, S., Gur, S., Zeltyn, S., Mavrogiorgos, K., Eliav, R., & Kyriazis, D. (2026). Self-Explaining Neural Networks for Business Process Monitoring. ICSBT 2026 – 23rd International Conference on Smart Business Technologies, 19-20 July 2026. Porto, Portugal. doi:10.48550/arXiv.2503.18067
