Hybrid Human Artificial Collective Intelligence in Open-Ended Decision Making

HACID开发了一种新的混合集体智能，用于为面临复杂的开放式问题的专业人员提供决策支持，促进参与、公平和信任。提出了一种基于结构化领域知识的决策支持系统(HACID-DSS)，该系统从科学文献和灰色文献等现有数据源中半自动地组装成领域知识图(DKG)。在给定所述领域内的特定案例的情况下，咨询专家池以(I)提取支持证据并丰富其，生成案例知识图(CKG)作为DKG的子集，以及(Ii)提供问题的一个或多个解决方案。利用CKG，HACID-DSS将专家的建议收集到一个集体解决方案中，该解决方案汇集了个人意见，并用机器生成的建议扩展了这些意见。通过这种方式，HACID在开放式问题中利用了人群的智慧，依赖于基于支持证据的可追溯过程，以获得更好的可解性。提出了一套评估方法来处理无法获得基本事实的领域，证明了该方法在广泛的应用领域中的适用性。两个令人信服的案例研究为实现联合国可持续发展目标提供了示范：众包医疗诊断和气候服务以促进城市适应。

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.