The broader impact/commercial potential of this I-Corps project is the development of an enterprise performance management (EPM) platform for investors, customers, suppliers, employees, and the community. The technology aims to broaden the scope of knowledge from financial-centric performance to an interdisciplinary framework of economic, social, psychological, and physical well-being concerning all stakeholders. In addition, the technology may democratize artificial intelligence (AI) to ordinary organizational managers who may not possess sophisticated analytics skills. The current AI models lack interactive and intuitive storytelling. Matching the hierarchical clustering of data with a causal knowledge graph, the proposed technology will prepare user data in a way that mimics a general manager’s intuitive thinking. The technology addresses a commercial gap in the market - that of a lack of prescriptive capability, that is, telling end-users what they should do. Data may be collected from different sources in an organization, so they are fragmented and the causal links are lost. The external source of a causal knowledge graph fills the gap by presenting and interpreting the hidden causal links in EPM data. The project seeks to help executives to prescribe interventions to enhance the well-being of all stakeholders.
This I-Corps project is based on the development of a knowledge graph embeddings-based platform for statistical and machine learning models of enterprise performance management (EPM) data. The technology is designed to engage natural language processing models to convert a massive volume of scientific research in organizational science into a causal knowledge graph, which will be embedded into a visual analytics platform to structure and interpret enterprise management data. The goal is to help EPM users by explaining the hidden causal pathways visually and intuitively to enable improvements in organizational management. The proposed technology combines research outcomes across organizational and computer sciences and involves two innovations: a scientific knowledge graph on causes-and-effects related to organizational performance and a new knowledge graph embeddings-based visualization technique to enable explainable AI (XAI). Hierarchical clustering is used to explicate the descriptions of variables in data and organize these descriptions. Causal hypotheses are automatically developed based on the known causal links in the knowledge graph and then empirically tested in statistical and machine learning models.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
