Openfund

CORDIS · AIXPERT

An agentic, multi-layer, GenAI-powered backbone to make an AI system explainable, accountable, and transparent

Code
AIXPERT
Programme
HORIZON
Call
HORIZON-CL4-2024-HUMAN-03-02
Status
SIGNED
Period
2025-06-01 – 2028-05-31
Total cost (whole consortium)7 499 754 €
EU contribution (whole consortium)7 499 754 €
EU contribution to Bulgarian participants67 906 €

Official data · CORDIS · synced 2026-10-18 03:29 · official record

Participants

OrganisationsRoleCountryGrant
ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIScoordinatorEL873 750 €
УНИВЕРСИТЕТ ГРЬОНИНГЕНparticipantNL753 750 €
SORBONNE UNIVERSITEparticipantFR631 875 €
BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACIONparticipantES545 000 €
STICHTING AMSTERDAM UMCparticipantNL507 629 €
D.TSAKALIDIS-G.DOMALIS OEparticipantEL499 875 €
FURHAT ROBOTICS ABparticipantSE490 625 €
UNIVERSITAT DE BARCELONAparticipantES477 250 €
IOTAM INTERNET OF THINGS APPLICATIONS AND MULTI LAYER DEVELOPMENT LTDparticipantCY414 875 €
KYKLOS LTDparticipantUK411 562 €
PHILIPS CONSUMER LIFESTYLE BVparticipantNL405 750 €
WORKABLE SOFTWARE SINGLE MEMBER PRIVATE COMPANYparticipantEL383 750 €
INFINITIVITY DESIGN LABSparticipantFR365 000 €
Vector Institute - Institut VecteurparticipantCA341 562 €
HEXACORP GREECE MONOPROSOPI ANONYMI ETAIREIAparticipantEL329 594 €
ALPHATHETA MUSIC BULGARIA EOODparticipantBG67 906 €
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRSthird partyFR0 €
MARTEL GMBHassociated partnerCH

Description

The AIXPERT project proposes a novel, comprehensive approach to developing explainable, accountable, and transparent AI systems. At its core, the project introduces an adaptable, situation-aware AI-agentic platform capable of encapsulating various AI models, regardless of their underlying architecture. This architecture-agnostic approach significantly enhances the trustworthiness of AI systems by providing a consistent framework for explainability and accountability across different model types. The project aims to address challenges in AI explainability, transparency, accountability, autonomy, and robustness by integrating multi-agent systems with multimodal foundation models and leveraging real-time human feedback. This integration enhances AI system trustworthiness and user-friendliness while maintaining flexibility in the choice of underlying AI models. The project will be implemented in a multi-layered architecture, with each layer focusing on specific aspects of AI interpretability and explainability: the Agent-World Interface Layer will define and coordinate AI agents, their situational awareness, and their interactions with real-world knowledge sources; the Dialogue Mediation Layer will control user-agent and agent-agent communications; and the Cognitive Foundation Layer will provide the base capabilities for the system based on explainable multimodal foundation models. The project will also focus on developing a framework for assessing AI trustworthiness, demonstrating the value of explainable AI through pilot demonstrations in healthcare, recruitment services, manufacturing, educational robotics, and creative industries, and ensuring the sustainability of its results. AIXPERT envisions delivering AI solutions that are not only transparent and ethical but also sustainable and adaptable to diverse user needs, ultimately fostering greater trust in AI across multiple industries and societal sectors.

Official data · CORDIS · synced 2026-10-18 03:29 · official record · Licence: © European Union, CC BY 4.0 (Commission reuse policy).