This section points to informative material and initiatives presenting HACID achievements.


Hybrid Collective Intelligence for Decision Support in Complex Open-Ended Domains

Title:Hybrid Collective Intelligence for Decision Support in Complex Open-Ended Domains

Author:Vito Trianni, Andrea Giovanni Nuzzolese, Jaron Porciello, Ralf H. J. M. Kurvers, Stefan M. Herzog, Gioele Barabucci, Aleksandra Berditchevskaia, Fai Fung

Publication:Volume 368 of Frontiers in Artificial Intelligence and Applications. HHAI 2023: Augmenting Human Intellect, pages 124-137, IOS Press


Human knowledge is growing exponentially, providing huge and sometimes contrasting evidence to support decision making in the realm of complex problems. To fight knowledge fragmentation, collective intelligence leverages groups of experts (possibly from diverse domains) that jointly provide solutions. However, to promote beneficial outcomes and avoid herding, it is necessary to (i) elicit diverse responses and (ii) suitably aggregate them in a collective solution. To this end, AI can help with dealing with large knowledge bases, as well as with reasoning on expert-provided knowledge to support decision-making. A hybrid human-artificial collective intelligence can leverage the complementarity of expert knowledge and machine processing to deal with complex problems. We discuss how such a hybrid human-artificial collective intelligence can be deployed to support decision processes, and we present case studies in two different domains: general medical diagnostics and climate change adaptation management.

Open access

Automating hybrid collective intelligence in open-ended medical diagnostics

Title:Automating hybrid collective intelligence in open-ended medical diagnostics

Author:Ralf H. J. M. Kurvers, Andrea Giovanni Nuzzolese, Alessandro Russo, Gioele Barabucci, Stefan M. Herzog, and Vito Trianni

Publication:PNAS 120(34):e2221473120


In the United States, an estimated 250,000 people die annually from preventable medical errors, many of which originate during the diagnostic process. A powerful approach to increase diagnostic accuracy is to combine the diagnoses of multiple diagnosticians. However, we lack methods to aggregate independent diagnoses in general medical diagnostics. Using knowledge engineering methods, we introduce a fully automated solution to this problem. We tested our solution on 1,333 medical cases, each of which was independently diagnosed by ten diagnosticians. Our solution substantially increases diagnostic accuracy: Single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. These results demonstrate that collective intelligence can reduce diagnostic errors, promoting health services and trust in the global medical community.

Open access

Public deliverables

D1.1 — IPR Management Plan

Title:D1.1 — IPR Management Plan


Publication:HACID web


Download the document here

Talks and presentations

IDEAL-IST webinars: Tips from a coordinator

Webinar organised by the IDEAL-IST network.

The webinar hosted three coordinators from European Projects funded by the Horizon Europe programme under Cluster 4 (Digital, Industry and Space) to share their experience in the proposal preparation and project management. Vito Trianni presented the HACID project and the related challenges.