In diverse industrial domains, data with highly complex objects, events, and relationships are collected and pre-processed through internal and external IoT sensing. Machinelearning-based convergence modeling technologies such as visualization for observation after data cleaning and processing; model-based error detection for estimation of causal relationships; fault prediction; and model evaluation have seen rapid development. As such, the paradigm has shifted from the consideration of modeling performance to explainable convergence computing reengineering to analyze the ground of a model. This paradigm shift has resulted in active research on the explainability of convergence data quality, model prediction, and performance. This approach is expected to simplify the procedure of verifying a value creation process and increase efficiency, thus being applicable in the development of convergence computing. This in turn will enable convergence modeling to draw significant knowledge in securing related technologies in different industrial domains.

New Directions in Convergence Computing was proposed to share and discuss research performance and solutions that both researchers and working-level persons achieved to find new convergence directions of explainable artificial intelligence. It is expected to draw new directions and create added values to improve people’s quality of life through