论文标题
了解我们的人民
Understanding Our People at Scale
论文作者
论文摘要
人类心理学在组织绩效中起着重要作用。但是，由于心理复杂性，不可预测的动态和缺乏数据，了解我们的员工是一项艰巨的任务。本文利用基于证据的心理学知识，提出了一种混合机器学习以及基于本体的推理系统，用于大规模检测人类心理伪像。这种独特的体系结构在系统的处理速度和解释能力之间提供了平衡。图形科学和/或模型管理系统可以进一步消耗系统输出，以优化业务流程，了解团队动态，预测内部威胁，管理才能以及其他。
Human psychology plays an important role in organizational performance. However, understanding our employees is a difficult task due to issues such as psychological complexities, unpredictable dynamics, and the lack of data. Leveraging evidence-based psychology knowledge, this paper proposes a hybrid machine learning plus ontology-based reasoning system for detecting human psychological artifacts at scale. This unique architecture provides a balance between system's processing speed and explain-ability. System outputs can be further consumed by graph science and/or model management system for optimizing business processes, understanding team dynamics, predicting insider threats, managing talents, and beyond.
