Advances in Trustworthy XAI: Methodologies, Benchmarking & Diverse Data Contexts

Aim and Scope

This special session addresses the crucial need for methodological rigor and standardized benchmarking in Explainable AI (XAI) across diverse data modalities (image, audio, tabular, multimodal). We invite research that develops, compares, and rigorously evaluates novel XAI techniques to strengthen transparency and trust in computational intelligence systems.

WCCI provides an ideal venue for this discussion, as it is the world’s premier event in computational intelligence, resulting from the convergence of IJCNN, IEEE CEC, and FUZZ-IEEE.

Topics of Interest

• Explainable and Transparent AI (XAI) for Image and Audio Analysis
• Interpretable Deep Learning for Tabular and Array Data
• Multimodal Explainability
• Novel Visualization Techniques
• Human-in-the-loop Evaluation for XAI
• Algorithmic Audits and Trustworthy AI
• Bias Mitigation in Explanations
• Causality and Counterfactuals
• XAI in Fuzzy Systems and Evolutionary Computation
• Benchmarking and Metrics for Explanations

Submission & Publication Information

Submitted papers will be peer-reviewed with the same criteria as other WCCI tracks. All accepted papers will be included in the WCCI 2026 proceedings and published in the IEEE Xplore Digital Library.

Papers must be submitted through the WCCI 2026 submission portal: WCCI 2026 Website

Important Dates & Deadlines

Committee