A European AI On Demand Platform and Ecosystem

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AI4EU is the European Union’s landmark Artificial Intelligence project, which seeks to develop a European AI ecosystem, bringing together the knowledge, algorithms, tools and resources available and making it a compelling solution for users. Involving 80 partners, covering 21 countries, the €20m project kicked off in January 2019 and will run for three years. AI4EU will unify Europe’s Artificial Intelligence community. It will facilitate collective work in AI research, innovation and business in Europe. By sharing AI expertise, knowledge and tools with the Platform, AI4EU will make AI available to all.

Mobilize the entire European AI community to make AI promises real for European Society and Economy
Create a leading collaborative AI European platform to nurture economic growth.

To achieve these goals, the project includes a diverse set of tasks and an ambitious set of activities.

• Create Europe’s leading AI On-Demand-Platform that is open and sustainable

• Bring stakeholders together through high-profile conferences and virtual events

• Develop a relevant, comprehensive and stimulating Strategic Agenda for European AI

• Establish an Ethics Observatory to ensure development of human-centred AI

• Roll out of €3m in Cascade Funding

AI4EU will nurture more adequacy between business needs and research results and accelerate growth. It will help the European Community to become a global leader in both highly advanced and human-centred AI, promising cutting-edge breakthroughs in this pivotal technological arena.

Guidotti, R., and G. Rossetti, "“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network", Formal Methods. FM 2019 International Workshops, Cham, Springer International Publishing, 2020//.
Naretto, F., R. Pellungrini, A. Monreale, F. Maria Nardini, and M. Musolesi, "Predicting and Explaining Privacy Risk Exposure in Mobility Data", Discovery Science, Cham, Springer International Publishing, 2020//.
Lampridis, O., R. Guidotti, and S. Ruggieri, "Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars", Discovery Science, Cham, Springer International Publishing, 2020//.
Setzu, M., R. Guidotti, A. Monreale, and F. Turini, "Global Explanations with Local Scoring", Machine Learning and Knowledge Discovery in Databases, Cham, Springer International Publishing, 2020//.
Guidotti, R., A. Monreale, S. Matwin, and D. Pedreschi, "Black Box Explanation by Learning Image Exemplars in the Latent Feature Space", Machine Learning and Knowledge Discovery in Databases, Cham, Springer International Publishing, 2020//.
Panigutti, C., R. Guidotti, A. Monreale, and D. Pedreschi, "Explaining multi-label black-box classifiers for health applications", International Workshop on Health Intelligence: Springer, 2019.
Guidotti, R., A. Monreale, and L. Cariaggi, "Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers", Pacific-Asia Conference on Knowledge Discovery and Data Mining: Springer, 2019.
Guidotti, R., and S. Ruggieri, "On The Stability of Interpretable Models", 2019 International Joint Conference on Neural Networks (IJCNN): IEEE, 2019.
Guidotti, R., A. Monreale, and D. Pedreschi, "The AI black box Explanation Problem", ERCIM NEWS, no. 116, pp. 12–13, 2019.


Premio Internazionale Tecnovisionarie® 2021

Il premio Le Tecnovisionarie 2021 sul tema Intelligenza Artificiale BigData è stato consegnato a Fosca Giannotti dalla Presidente del CNR con la seguente motivazione:

MALOTEC Seminar - Riccardo Guidotti: Evaluating Local Explanation Methods on Ground Truth 09/04/2021

Riccardo is an Assistant Professor at the Department of Computer Science, University of Pisa, and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group with the Information Science and Technology Institute

Web Site
Start Date
1 January 2019
End Date
31 December 2021
European Commission
European Project
Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR)