Trusted AI for Transparent Public Governance fostering Democratic Values

HORIZON.2.2HORIZON-RIAID: 101094905
EC Contribution
€30,000
Consortium Size
14 orgs
Summary

AI4Gov is a joint effort of policy makers, public institutions / organizations, legal, Social Science and Humanities and Big Data/AI experts to unveil the potentials of Artificial Intelligence (AI) and Big Data technologies for developing evidence-based innovations, policies, and policy recommendations to harness the public sphere, political power, and economic power for democratic purposes. The project will also uphold fundamental rights and values standards of individuals when using AI and Big Data technologies. Hence, the project aims to contribute to the promising research landscape that seeks to address ethical, trust, discrimination, and bias issues by providing an in-depth analysis and solutions addressing the challenges that various stakeholders in modern democracies are faced with when attempts are made to mitigate the negative implications of Big Data and AI. In this direction, the project will introduce solutions and frameworks towards a two-fold sense, to facilitate policymakers on the development of automated, educated and evidence-based decisions and to increase the trust of citizens in the democratic processes and institutions. Moreover, the project will leverage the capabilities of state-of-the-art tools for providing un-bias, discrimination-free, fair, and trusted AI. These tools will be validated in terms of their ability to provide technical and/or organisational measures, causal models for bias and discrimination, and standardized methodologies for achieving fairness in AI.

Consortium (14)

Project Results (31)

Source: CORDIS, the EU research results database.

Publications (17)
Combining Explainable Artificial Intelligence (Xai) With Blockchain Towards Trustworthy Data-Driven Policies
2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)· 2025DOI
Konstantinos Mavrogiorgos, Shlomit Gur, Nikolaos Kalantzis, Konstantinos Tzelaptsis, Xanthi S. Papageorgiou, Andreas Karabetian, Georgios Manias, Argyro Mavrogiorgou, Dimosthenis Kyriazis, Celia Parra
How well can a large language model explain business processes as perceived by users?
Data & Knowledge Engineering· 2025DOI
Dirk Fahland, Fabiana Fournier, Lior Limonad, Inna Skarbovsky, Ava J.E. Swevels
Selecting the Right Llm for Egov Explanations
2025 Eleventh International Conference on eDemocracy & eGovernment (ICEDEG)· 2025DOI
Lior Limonad, Fabiana Fournier, Hadar Mulian, George Manias, Spiros Borotis, Danai Kyrkou
The WHY in Business Processes: Discovery of Causal Execution Dependencies
KI - Künstliche Intelligenz· 2025DOI
Fabiana Fournier; Lior Limonad; Inna Skarbovsky; Yuval David
The WHY in Business Processes: Unification of Causal Process Models
Lecture Notes in Business Information Processing, Business Process Management Forum· 2025DOI
Yuval David, Fabiana Fournier, Lior Limonad, Inna Skarbovsky
Time Series Forecasting for Touristic Policies
ITISE 2025· 2025DOI
Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimitrios Apostolopoulos, Andreas Menychtas, Dimosthenis Kyriazis
Towards a Benchmark for Causal Business Process Reasoning with LLMs
Lecture Notes in Business Information Processing, Business Process Management Workshops· 2025DOI
Fabiana Fournier, Lior Limonad, Inna Skarbovsky
Bias in Machine Learning: A Literature Review
Applied Sciences· 2024DOI
Konstantinos Mavrogiorgos; Athanasios Kiourtis; Argyro Mavrogiorgou; Andreas Menychtas; Dimosthenis Kyriazis
Bridging Global Disparities: An Analytics Pipeline for Detecting Bias and Incompleteness in Rare Diseases Datasets
Meeting abstracts from the 12th European Conference on Rare Diseases and Orphan Products· 2024DOI
Alenka Guček, Matej Kovačič, Tanja Zdolšek Draksler
EXPLAIN YOURSELF, BRIEFLY! SELF-EXPLAINING NEURAL NETWORKS WITH CONCISE SUFFICIENT REASONS
The Thirteenth International Conference on Learning Representations (ICLR)· 2024
Shahaf Bassan, Ron Eliav, Shlomit Gur
eXplainable Random Forest
Proceedings of Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024)· 2024
Guy Amit, Shlomit Gur
Fostering Fundamental Human Rights and Trustworthiness though the Utilization of Emerging Technologies: the AI4Gov Platform
Proceedings from the 2024 Global Conference on AI and Human Rights· 2024
George Manias; Spiros Borotis; Charalampos Chatzimallis; Tanja Zdolsek Draksler; Alenka Gucek; Fabiana Fournier; Andreas Karabetian; Dimitris Kotios; Matej Kovacic; Danai Kyrkou; Lior Limonad; Konstantinos Mavrogiorgos; Dimitris Ntalaperas; Xanthi S. Papa
Mitigating Bias in Time Series Forecasting for Efficient Wastewater Management
2024 7th International Conference on Informatics and Computational Sciences (ICICoS)· 2024DOI
Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Alenka Gucek, Andreas Menychtas, Dimosthenis Kyriazis
A Question Answering Software for Assessing AI Policies of OECD Countries
The 4th European Symposium on Software Engineering (ESSE 2023)· 2023DOI
Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Georgios Manias, Dimosthenis Kyriazis
"G. Manias et al., ""AI4Gov: Trusted AI for Transparent Public Governance Fostering Democratic Values,"" 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Pafos, Cyprus, 2023, pp. 548-555, do"
In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)DOI
George Manias, Dimitris Apostolopoulos, Sotiris Athanassopoulos, Spiros Borotis, Charalampos Chatzimallis, Theodoros Chatzipantelis, Marcelo Corrales Compagnucci, Tanja Zdolsek Draksler, Fabiana Fournier, Magdalena Goralczyk, Alenka Gucek, Andreas Karabet
Multilingual Classification of AI-Oriented Policy Documents based on Bias Types
Data for Policy 2025 (DfP’25) - Europe Book of Abstracts
George Manias, Chrysa Agapitou, Nemania Borovits, Alenka Guček, Andreas Karabetian, Matej Kovacic, Konstantinos Mavrogiorgos, Tanja Zdolšek Draksler, Willem-Jan van den Heuvel, Dimosthenis Kyriazis
Self-Explaining Neural Networks for Business Process Monitoring
ICSBT 2026 – 23rd International Conference on Smart Business TechnologiesDOI
Shahaf Bassan, Shlomit Gur, Sergey Zeltyn, Konstantinos Mavrogiorgos, Ron Eliav, Dimosthenis Kyriazis
Deliverables (13)
Other Results (1)
Periodic Reporting for period 1 - AI4Gov (Trusted AI for Transparent Public Governance fostering Democratic Values)