Worldview, Experience, and Metadata: Operationalizing Integrative Levels for Transdisciplinary Knowledge Systems

1 Description

Drawing from a longitudinal dataset of human–AI interaction and applied system design, the workshop presents an operational model for embedding worldview-aware inputs into metadata systems. The goal is to support human-centered, ethical, and interoperable metadata for AI and transdisciplinary research.

The exercises will allow for narrative exploration of human values and variations through definitions, classification, and framing experiments. Differences in outcomes are analyzed to identify patterns of bias, inconsistency, and divergence in metadata structures.

2 Objectives

Participants will:

• Examine how worldview and lived experience influence classification decisions
• Understand how definitional assumptions impact metadata structures
• Apply integrative levels to organize knowledge across disciplines
• Identify how bias emerges in tagging and categorization
• Explore methods for embedding human perspective into metadata systems
• Evaluate implications for AI alignment, interoperability, and reproducibility

3 Format

Interactive, in-person workshop including:

• Interactive and narrative real-time definition mapping and tagging exercises
• Small-group classification activities, scenario-based decision-making, and comparative analysis
• Tagging will consist of 60% analog, 30% traditional tech, 10% AI

4 Event Plan (3 Hours)

1. Introduction (10 min)

   1. The problem: AI readiness, media & data literacy, metadata challenges, and transdisciplinary fragmentation (i.e., streetlight effect, magical thinking, predisposition to bias, interoperability, reproducibility)
   2. The journey from self-awareness to global understanding: framing the role of human values in metadata systems

2. Framing, Definitions & Classifications (20 min)

   1. Comparison of rigid vs open definitions & impact on classification systems
   2. Group tagging of entities using DCMI core & comparison across groups

3. Survival Exercise - Individual vs Group Decision-Making (30 min)

   1. Analysis of lived experience, culture, and disciplines
   2. Connection to metadata construction

4. Worldview Mapping (30 min)

   1. Individual mapping of observed influences to determine impact of lived experiences and cultural perspectives
   2. Link worldview to classification behavior & identify bias patterns

5. Media Literacy & AI Readiness Integration (30 min)

   1. Framing effects and statistical interpretation
   2. Influence on classification and bias

6. Consciousness Definition Mapping Exercise (30 min)

   1. Understanding research and leadership bias
   2. Participants define “consciousness”, responses converted into live metadata tags

7. Global Understanding and Epistemic Integrity (20 min)

   1. Structuring worldview with DCMI Core
   2. Operationalizing integrative levels using the un-assumptions methodology
   3. Embedding worldview into metadata systems
   4. Implications for interoperability and reproducibility of AI metadata tagging and transdisciplinary research

8. Conclusion (10 min)

   1. Key takeaways
   2. Open discussion