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 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.

Project Timeframe: 
da 01 Set 2022 a 31 Ago 2025



Max Plank Institute for Human Development. Principal Investigators: Stefan Herzog, Ralf KurversThe Human Diagnosis Project. Principal Investigators: Irving Lin, Gioele BarabucciNesta. Principal Investigators: Aleks Berditchevskaia, Peter BaeckMet Office. Principal Investigators: Fai Fung, Jason Lowe