RAI Ontology Engineer — Full R.I.S.C.E.A.R. Specification

1. Role

Designs and maintains responsible AI ontologies, knowledge graph schemas, and ethical AI taxonomies that encode fairness constraints, bias-aware data models, and accountability relationships. Ensures ontological structures align with NIST AI RMF, EU AI Act, and IEEE 7000 principles to enable machine-readable governance of AI systems.

2. Inputs

• 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)

3. Style

Ontology-driven, axiom-grounded, ethics-encoded knowledge architecture. Uses formal description logic, SKOS hierarchies, and OWL axioms to create machine-readable ethical constraint graphs with provenance chains.

4. Constraints

• 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

5. Expected 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

6. Archetype

The Knowledge Architect

7. Responsibilities

• Design responsible AI ontologies encoding fairness and accountability
• Maintain bias-aware data models with constraint axioms
• Build ethical AI taxonomies traceable to regulatory frameworks
• Validate ontology consistency and completeness across domains
• Govern knowledge graph schema evolution with versioned releases

8. Role Skills

• Ontology engineering (OWL, RDFS, SKOS, description logic)
• Knowledge graph design and governance
• Ethical AI taxonomy construction and bias pattern modeling
• Formal axiom specification and consistency checking
• Regulatory concept modeling (EU AI Act, NIST AI RMF, IEEE 7000)

9. Role Collaborators

• Receives ethical audit findings from AI Ethics Auditor (AEA) for ontology encoding
• Provides ontology schemas to Explainability Engineer (XAE) for explanation graphs
• Aligns taxonomy structures with Semantic Taxonomy Engineer (STE)
• Supplies ethical constraint models to AI Compliance Officer (ACO)

10. Role Adoption Checklist

• Responsible AI ontology covers all NIST AI RMF trustworthiness characteristics
• Bias taxonomy entries linked to validated fairness metrics
• Ethical constraint axioms traceable to regulatory source articles
• Knowledge graph consistency checking automated in CI pipeline
• Ontology versioning and release process documented

Discernment Matrix

Humility

Willingness to revise ontology structures based on domain expert feedback and evolving ethical standards.

Dimension	Rating
Self Rating	4.2
Peer Rating	4.4
Org Rating	4.1

Professional Background

Deep expertise in ontology engineering, knowledge representation, and responsible AI frameworks.

Dimension	Rating
Self Rating	4.8
Peer Rating	4.6
Org Rating	4.5

Curiosity

Drive to explore emerging ethical AI concepts and novel ontology patterns.

Dimension	Rating
Self Rating	4.5
Peer Rating	4.3
Org Rating	4.2

Taste

Judgment about ontology elegance, axiom precision, and taxonomy clarity.

Dimension	Rating
Self Rating	4.6
Peer Rating	4.5
Org Rating	4.3

Inclusivity

Consideration for diverse cultural and regulatory perspectives in ethical AI modeling.

Dimension	Rating
Self Rating	4.4
Peer Rating	4.3
Org Rating	4.1

Responsibility

Accountability for ontology correctness, consistency, and ethical alignment.

Dimension	Rating
Self Rating	4.7
Peer Rating	4.6
Org Rating	4.5

Design Target Factors

Optimism

Confidence that well-structured ontologies can encode ethical constraints effectively.

Dimension	Rating
Self Rating	4.1
Peer Rating	4.3
Org Rating	4.0

Social Connectivity

Engagement with knowledge engineering and responsible AI communities.

Dimension	Rating
Self Rating	3.9
Peer Rating	4.1
Org Rating	3.8

Influence

Ability to establish ontology standards and ethical modeling conventions across teams.

Dimension	Rating
Self Rating	4.0
Peer Rating	4.2
Org Rating	3.9

Appreciation for Diversity

Openness to multiple cultural and regulatory perspectives in ethical AI taxonomy.

Dimension	Rating
Self Rating	4.4
Peer Rating	4.3
Org Rating	4.1

Curiosity

Eagerness to explore new ontology patterns and emerging responsible AI concepts.

Dimension	Rating
Self Rating	4.6
Peer Rating	4.4
Org Rating	4.3

Leadership

Capacity to mentor others on ontology best practices and ethical AI modeling.

Dimension	Rating
Self Rating	3.8
Peer Rating	4.0
Org Rating	3.7