mUlti-Level Trustworthiness to IMprove the Adoption of hybrid arTificial intelligencE

Informazioni relative al progetto

ULTIMATE

ID dell’accordo di sovvenzione: 101070162

DOI

10.3030/101070162

Progetto chiuso

Data della firma CE 12 Giugno 2022

Data di avvio 1 Ottobre 2022

Data di completamento 30 Settembre 2025

Finanziato da

• Digital, Industry and Space

Costo totale

€ 3 529 112,50

Contributo UE

€ 3 529 112,50

Coordinato da

THALES
France

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

Websites, patent fillings, videos etc. (1)

Project public website and social media

The ULTIMATE public website will provide information about the project, publications, news, and events.

Ethics Requirements (1)

OEI - Requirement No. 1

"The ""Hybrid AI for Robotic navigation and collaborative work"" demonstrator considers the detection and precise tracking of human workers. The ethics advisor should ensure that the rights of the workers are preserved concerning privacy and data protection and that they are truly free to accept or reject participation to the research experiment.A first report on this topic should be provided before the start of WP5 (meaning M12)."

Pubblicazioni

Conference proceedings (5)

Real-world Application Facilitating Trustworthy Human-Robot Collaboration with Innovative Explainable Neuro-symbolic Reasoning

Autori: R. Kozik, S. Buś, G. Gawdzik, M. Pawlicki, A. Pawlicka and M. Choraś
Editore: IEEE

Towards Quality Measures for xAI algorithms: Explanation Stability

Autori: Marek Pawlicki
Pubblicato in: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023, ISBN 979-8-3503-4503-2
Editore: IEEE
DOI: 10.1109/DSAA60987.2023.10302535

Real-Time Anomaly Detection and Categorization for Satellite Reaction Wheels

Autori: Alejandro Penacho Riveiros, Yu Xing, Nicola Bastianello, Karl H. Johansson
Pubblicato in: 2024 European Control Conference (ECC), 2024
Editore: IEEE
DOI: 10.23919/ECC64448.2024.10591184

How Explainable Is Explainability? Towards Better Metrics for Explainable AI

Autori: Pawlicka, A., Pawlicki, M., Kozik, R., Kurek, W., Choraś M.
Pubblicato in: Research and Innovation Forum (RIIFORUM) 2023, 2024, ISBN 978-3-031-44721-1
Editore: Springer
DOI: 10.1007/978-3-031-44721-1_52

Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case

Autori: D. Longuet, A. Elouazzani, A. Penacho Riveiros, N. Bastianello
Pubblicato in: Proceedings Seventh International Workshop on Formal Methods for Autonomous Systems, ISSN 2075-2180
Editore: Electronic Proceedings in Theoretical Computer Science
DOI: 10.48550/ARXIV.2509.03948

Book chapters (3)

Supporting Human-Robot Collaboration and Safety with the Proposed Explainable Neuro-Symbolic Reasoning

Autori: Rafał Kozik, Aleksandra Pawlicka, Marek Pawlicki, Michał Choraś
Pubblicato in: Lecture Notes in Computer Science, Availability, Reliability and Security, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-00642-4_15

ULTIMATE Project Toolkit for Robotic AI-Based Data Analysis and Visualization

Autori: Rafał Kozik, Damian Puchalski, Aleksandra Pawlicka, Szymon Buś, Jakub Główka, Krishna Chandramouli, Marco Tiemann, Marek Pawlicki, Rafał Renk, Michał Choraś
Pubblicato in: Lecture Notes in Computer Science, Intelligent Information and Database Systems, 2024
Editore: Springer Nature Singapore
DOI: 10.1007/978-981-97-4985-0_4

Ensuring Trustworthiness of Hybrid AI-Based Robotics Systems

Autori: Eguia, Alexander; Quintano, Nuria; Marsh, Irina; Barreteau, Michel; Główka, Jakub; Sprońska, Agnieszka
Pubblicato in: 2024
Editore: Springer Nature
DOI: 10.1007/978-3-031-76428-8_27

Peer reviewed articles (2)

Neurocomputing

Autori: M. Pawlicki, A. Pawlicka, R. Kozik, M. Choraś
Pubblicato in: Neurocomputing, Numero 950, 2024, ISSN 0925-2312
Editore: Elsevier
DOI: 10.1016/j.neucom.2024.127759

On the black-box explainability of object detection models for safe and trustworthy industrial applications

Autori: Alain Andres; Aitor Martinez-Seras; Ibai Laña; Javier Del Ser
Pubblicato in: Results in Engineering, 2024, ISSN 2590-1230
Editore: Elsevier
DOI: 10.48550/ARXIV.2411.00818