Workshop: Worldview, Experience, and Metadata

This workshop explores how worldview, lived experience, and definitional assumptions shape metadata, classification systems, and transdisciplinary research interoperability. Under the theme of meaning-driven AI, the session examines how metadata can encode human values, cultural context, and experiential knowledge to improve alignment, transparency, and bias mitigation in AI systems. Across disciplines, concepts such as consciousness, intelligence, and agency are inconsistently defined and often anthropocentric, contributing to fragmentation in knowledge organization. Rather than advancing a fixed definition, this workshop focuses on how definitional variation affects classification outcomes. Participants will engage in real-time exercises to map their perspectives, translate them into metadata tags, and observe how bias emerges structurally in categorization systems. The workshop integrates worldview mapping, media literacy principles, and scenario-based group decision-making, including a survival simulation exercise to compare individual and collective classification behavior. Drawing on an operational framework and longitudinal human–AI interaction data, the session demonstrates how embedding worldview-aware inputs into metadata systems can support more adaptive, human-centered, and interoperable knowledge infrastructures. This approach contributes to meaning-driven AI by providing practical methods for aligning metadata systems with human values across domains.