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HACID

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Closed

Hybrid Human Artificial Collective Intelligence in Open-Ended Decision Making

HACID develops a novel hybrid collective intelligence for decision support to professionals facing complex open-ended problems, promoting engagement, fairness and trust. A decision support system (HACID-DSS) is proposed that is ba... see more

31/08/2025

CNR

2M€

Project Budget: 2M€

Project leader

CONSIGLIO NAZIONALE DELLE RICERCHE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2025-08-31

HORIZON-CL4-2021-DIGITAL-EMERGING-01-12: European Network of Excellence Cent... Expected Outcome:Proposal results are expected to contribute to all the following expected outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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Project duration: 35 months Date Start: 2022-09-01
End date: 2025-08-31

Project leader

CONSIGLIO NAZIONALE DELLE RICERCHE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 2M€

participation deadline Sin fecha límite de participación.

Project description HACID develops a novel hybrid collective intelligence for decision support to professionals facing complex open-ended problems, promoting engagement, fairness and trust. A decision support system (HACID-DSS) is proposed that is based on structured domain knowledge, semi-automatically assembled in a domain knowledge graph (DKG) from available data sources, such as scientific and gray literature. Given a specific case within the addressed domain, a pool of experts is consulted to (i) extract supporting evidence and enrich it, generating a case knowledge graph (CKG) as a subset of the DKG, and (ii) provide one or more solutions to the problem. Exploiting the CKG, the HACID-DSS gathers the expert advice in a collective solution that aggregates the individual opinions and expands them with machine-generated suggestions. In this way, HACID harnesses the wisdom of the crowd in open-ended problems, relying on a traceable process based on supporting evidence for better explainability. A set of evaluation methods is proposed to deal with domains where ground truth is not available, demonstrating the suitability of the proposed approach in a wide range of application domains. Demonstrations are provided in two compelling case studies contributing to the UN Sustainable Development Goals: crowd-sourcing medical diagnostics and climate services for urban adaptation.

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