Actualization of Causality-Based Transparency and Accuracy in System Modeling with Human-Machine Collaboration

Abstract

Considering the volumes of data sets and complexity of analysis in the future, a concept is needed to equip the researcher with a method to acquire causality, transparency and accuracy. We propose an extended process of human-machine collaboration in combining expert knowledge in the field of conceptual causality, variable selection and their combination, and inspection with computational precision to gain a method with a 3-step causality and transparency.

Keywords:

• Causality Acquisition
• Model Selection
• Health Management Technology
• Human-Machine Collaboration