Trusted AI for Transparent Public Governance fostering Democratic Values

Informazioni relative al progetto

AI4Gov

ID dell’accordo di sovvenzione: 101094905

DOI

10.3030/101094905

Progetto chiuso

Data della firma CE 11 Dicembre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Dicembre 2025

Finanziato da

• Culture, creativity and inclusive society

Costo totale

€ 3 000 000,00

Contributo UE

€ 3 000 000,00

Coordinato da

MAGGIOLI SPA
Italy

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Demonstrators, pilots, prototypes (8)

AI4Gov Holistic Regulatory Framework V1

will study and list common antecedents for biases, unfairness and non-inclusiveness and list of vulnerable (intersectional) groups by UC and design the HRF of the project (T2.2).

Decentralized Data Governance, Provenance and Reliability V1

will architect the decentralized Blockchain-based applications in governance and will introduce, monitor and revise the DGF across the complete lifecycle of the project.

Trustworthy, Explainable, and unbiased AI V1

Prototype implementation of AI, XAI, SAX, FML and real-time analytics infrastructures.

AI4Gov Holistic Regulatory Framework V2

will study and list common antecedents for biases, unfairness and non-inclusiveness and list of vulnerable (intersectional) groups by UC and design the HRF of the project (T2.2).

Policies Visualization Services V1

Software implementations of policy modelling/interpretation/monitoring, and visualization services.

Reference Architecture and Integration of AI4Gov Platform V1

will identify the key components of AI4Gov and define the interfaces and interactions between them. It will also describe the integrations between the toolkits, libraries, frameworks and components of the project.

Decentralized Data Governance, Provenance and Reliability V2

will architect the decentralized Blockchain-based applications in governance and will introduce, monitor and revise the DGF across the complete lifecycle of the project.

Policy Recommendation Toolkit V1

will deliver a populated version of the catalogue for policies models and associated datasets to be exploited for reuse in different domains (T3.4).

Documents, reports (5)

Assessment tools, training activities, best practice guide V1

will introduce a set of (self) assessment tools and checklists, describe the training plan and activities with respect to different groups. Moreover, they will provide the material used in envisioned training courses and MOOCs, and a best practice guide and blueprint will be delivered covering ethical and technical aspects of AI development processes.

Specification of UC Scenarios and Planning of Integration and Validation Activities V2

Report on co-creation activities and specification design of the different UC scenarios. AI4Gov technologies’ experimentation and evaluation upon the different UCs.

Specification of UC Scenarios and Planning of Integration and Validation Activities V1

Report on co-creation activities and specification design of the different UC scenarios. AI4Gov technologies’ experimentation and evaluation upon the different UCs.

Input papers to facilitate the workshops on awareness raising V1

will provide the input papers with data on existing awareness-raising strategies to mitigate AI bias and discrimination and to enhance inclusiveness, representation, participation, openness, pluralism, and tolerance.

Dissemination, Communication, Standardization Activities Report V1

report (living document) on the outcomes of T7.1, T7.2 and T7.3, that will be delivered periodically.

Pubblicazioni

Conference proceedings (7)

Mitigating Bias in Time Series Forecasting for Efficient Wastewater Management

Autori: Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Alenka Gucek, Andreas Menychtas, Dimosthenis Kyriazis
Pubblicato in: 2024 7th International Conference on Informatics and Computational Sciences (ICICoS), 2024
Editore: IEEE
DOI: 10.1109/ICICOS62600.2024.10636931

Combining Explainable Artificial Intelligence (Xai) With Blockchain Towards Trustworthy Data-Driven Policies

Autori: Konstantinos Mavrogiorgos, Shlomit Gur, Nikolaos Kalantzis, Konstantinos Tzelaptsis, Xanthi S. Papageorgiou, Andreas Karabetian, Georgios Manias, Argyro Mavrogiorgou, Dimosthenis Kyriazis, Celia Parra
Pubblicato in: 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), 2025
Editore: IEEE
DOI: 10.1109/DCOSS-IOT65416.2025.00157

Time Series Forecasting for Touristic Policies

Autori: Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimitrios Apostolopoulos, Andreas Menychtas, Dimosthenis Kyriazis
Pubblicato in: ITISE 2025, 2025
Editore: MDPI
DOI: 10.3390/CMSF2025011004

The WHY in Business Processes: Unification of Causal Process Models

Autori: Yuval David, Fabiana Fournier, Lior Limonad, Inna Skarbovsky
Pubblicato in: Lecture Notes in Business Information Processing, Business Process Management Forum, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-02929-4_3

Towards a Benchmark for Causal Business Process Reasoning with LLMs

Autori: Fabiana Fournier, Lior Limonad, Inna Skarbovsky
Pubblicato in: Lecture Notes in Business Information Processing, Business Process Management Workshops, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-78666-2_18

Selecting the Right Llm for Egov Explanations

Autori: Lior Limonad, Fabiana Fournier, Hadar Mulian, George Manias, Spiros Borotis, Danai Kyrkou
Pubblicato in: 2025 Eleventh International Conference on eDemocracy & eGovernment (ICEDEG), 2025
Editore: IEEE
DOI: 10.1109/ICEDEG65568.2025.11081620

A Question Answering Software for Assessing AI Policies of OECD Countries

Autori: Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Georgios Manias, Dimosthenis Kyriazis
Pubblicato in: The 4th European Symposium on Software Engineering (ESSE 2023), 2023
Editore: ACM
DOI: 10.1145/3651640.3651651

Peer reviewed articles (4)

Bias in Machine Learning: A Literature Review

Autori: Konstantinos Mavrogiorgos; Athanasios Kiourtis; Argyro Mavrogiorgou; Andreas Menychtas; Dimosthenis Kyriazis
Pubblicato in: Applied Sciences, 2024, ISSN 2076-3417
Editore: MDPI
DOI: 10.3390/APP14198860

Orphanet Journal of Rare Diseases

Pubblicato in: Orphanet Journal of Rare Diseases, 2024, ISSN 1750-1172
Editore: Springer Nature
DOI: 10.1186/S13023-024-03293-9

The WHY in Business Processes: Discovery of Causal Execution Dependencies

Autori: Fabiana Fournier; Lior Limonad; Inna Skarbovsky; Yuval David
Pubblicato in: KI - Künstliche Intelligenz, 2025, ISSN 0933-1875
Editore: Springer Nature
DOI: 10.48550/ARXIV.2310.14975

Data and Knowledge Engineering

Autori: Dirk Fahland; Fabiana Fournier; Lior Limonad; Inna Skarbovsky; Ava J.E. Swevels
Pubblicato in: Data and Knowledge Engineering, 2025, ISSN 0169-023X
Editore: Elsevier BV
DOI: 10.48550/ARXIV.2401.12846

Other (2)

The WHY in Business Processes: Discovery of Causal Execution Dependencies

Autori: Fournier, Fabiana; Limonad, Lior; Skarbovsky, Inna; David, Yuval
Pubblicato in: 2023
DOI: 10.48550/arxiv.2310.14975

How well can large language models explain business processes?

Autori: Dirk Fahland, Fabiana Fournier, Lior Limonad, Inna Skarbovsky, Ava J.E. Swevels
Pubblicato in: 2024
DOI: 10.48550/arXiv.2401.12846