HACID

HACID aims at harnessing the hybrid collective intelligence of human experts and AI systems to address open-ended problems—i.e., problems in which the solutions are not constrained to a (predefined, limited) set of alternatives. We aim to develop a general methodology and apply it to medical diagnostics and climate services.
In medical diagnostics, the identification of a disease from a set of symptoms may be particularly complex, as it deals with a large variety of possible diseases. Climate services represent a relatively new area of decision-making but already supported by large formal and informal bodies of knowledge, demanding the integration of multiple knowledge domains into local contexts.

A promising way to improve decision making in complex open-ended problems is exploiting collective intelligence (CI). HACID aims at developing a hybrid collective intelligence decision support system capable of providing support to evidence-based decision-making, and aggregating and expanding the solutions provided by multiple experts, ultimately providing higher efficacy and efficiency, as well as higher user satisfaction, explainability and trust. The proposed system leverages complementarities between domain expertise from humans and the AI ability of reasoning on and analyzing vast amounts of data. Using a participatory approach, HACID aims at deploying an AI system capable to deal with complex, high stakes application domains and decision-making contexts.