Charles Wan

Charles Wan serves as a Postdoctoral Research Fellow at the Center for Collective Learning, Corvinus Institute for Advanced Studies, Corvinus University of Budapest, following the completion of his PhD at Rotterdam School of Management, Erasmus University. His prior career as a commodities trader across global markets provides empirical grounding for his research on organizational decision processes amid AI adoption.

Wan's research investigates causal structures in human-organizational decision-making under statistical learning algorithms, focusing on how algorithmic explanations interact with human agency, causal reasoning, and information processing. Key contributions examine confirmation bias in explanation-driven decisions, disparate impacts from differential algorithmic robustness, and multi-objective agent dynamics across organizational levels. His interdisciplinary approach integrates artificial intelligence, cognitive science, and organizational theory to address critical challenges in explainable AI systems and their real-world implications for fairness and decision optimality.

Analysis of Wan's 2022-2023 publications reveals a cohesive research trajectory centered on human-AI decision tensions, particularly the trade-off between explainability and optimality. His work employs case studies and multi-agent modeling to demonstrate how explanations exacerbate representation biases, how robustness variations create fairness violations, and how organizational hierarchies mediate algorithmic impacts. This scholarship spans AI ethics conferences (AIES), artificial life (ALIFE), and explainable AI (xAI), establishing foundational insights for responsible AI deployment in organizational contexts.

At the Center for Collective Learning, Wan collaborates on collective intelligence initiatives exploring adaptive organizational structures for algorithmic decision support. Current projects investigate meta-screening mechanisms for human-algorithmic output integration and attentional dynamics in organizational settings, extending his research into practical frameworks for harmonizing human and machine decision capabilities.