Research Projects 

Soft Computing-based Causal Analysis and Fusion Study on Deep Learning and FNN for TAI(inTerpretable AI) and XAI(eXplainable AI)

(National Research Foundation, 2024~2028) 

Soft Computing–Based Causal Analysis and Fusion for Interpretable and Explainable AI

This research project focuses on developing interpretable and explainable AI frameworks by integrating deep learning with soft computing–based neural models through causal analysis and model fusion.
The goal is to move beyond black-box prediction and enable AI systems whose decision processes can be understood, analyzed, and trusted.

We study how learning dynamics, internal representations, and decision outcomes in deep neural networks can be causally explained and enhanced by combining them with mathematically structured, human-interpretable neural models.
Through this fusion, the project aims to provide theoretical insight, transparency, and robustness in AI systems while maintaining strong predictive performance.

The outcomes of this research contribute to the foundations of inTerpretable AI (TAI) and eXplainable AI (XAI), supporting reliable deployment of intelligent systems in high-stakes, real-world applications.