Hybrid Human Artificial Collective Intelligence in Open-Ended Decision Making

HACID开发了一种新型的混合集体智慧，为面临复杂开放式问题的专业人士提供决策支持，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）。给定所述领域内的特定案例，咨询专家库以（i）提取支持证据并丰富它，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将专家建议收集在一个集体解决方案中，该解决方案汇总了个人意见，并使用机器生成的建议对其进行扩展。通过这种方式，HACID在开放式问题中利用了群众的智慧，依靠基于支持证据的可追溯过程来获得更好的解释性。一组评估方法，提出了处理域地面真相是不可用的，证明了所提出的方法在广泛的应用领域的适用性。两个引人注目的案例研究为联合国可持续发展目标做出了贡献：众包医疗诊断和城市适应气候服务。

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.