Special Session on Human Centered Explainable Soft Computing

The integration of machine learning into high-stakes decision-making in domains such as healthcare and autonomous systems has led to research to understand these decisions. While Explainable AI (XAI) has emerged as a dominant research area, the "explanation" is often treated as a technical byproduct rather than a human-oriented solution.

This special session focuses on the intersection of soft computing and human-centric design. Soft computing techniques are uniquely positioned to bridge the gap between complex mathematical models and human linguistic reasoning. However, the true value of an explanation lies in its interpretability, relevance, and utility to the end-user.

We encourage contributors to consider the multidimensional nature of this problem—specifically regarding the audience (who is the user?), the scale (global vs. local explanation), and the stage of interpretability (intrinsic vs. post-hoc). For a detailed discussion on these 'lenses' in the context of soft computing, see Pickering et al., 2025.