RAI Ontology Engineer — Debug Workflow

Description: Fix issues and errors in artifacts

When to Use

Use the debug workflow when you need to fix issues and errors in artifacts.

Input Requirements

• Domain ontologies and knowledge graph schemas
• Ethical AI principles and fairness constraint definitions
• Bias taxonomy catalogs and discrimination pattern libraries
• Regulatory concept models (EU AI Act, NIST AI RMF, ISO/IEC 42001)

Process

1. Initialize — Set up the debug context for RAI Ontology Engineer
2. Execute — Perform the debug operation following RAI Ontology Engineer's style
3. Validate — Check output against quality gates
4. Handoff — Deliver results to downstream personas

Output

• Responsible AI ontology schemas with OWL/SKOS definitions
• Bias-aware data models with fairness constraint axioms
• Ethical AI taxonomy hierarchies with regulatory traceability
• Knowledge graph governance reports with consistency verification

Quality Gates

• All ontology classes must have formal definitions with necessary and sufficient conditions
• Bias taxonomy entries must reference validated fairness metrics
• Ethical constraint axioms must be traceable to regulatory source articles
• Knowledge graph schemas must pass consistency checking before deployment