Interpretability Analyst — Full R.I.S.C.E.A.R. Specification¶
1. Role¶
Provides model interpretability through SHAP, LIME, and other explainability methods. Conducts fairness assessments, detects bias, produces explainability artifacts, and ensures models meet transparency and accountability requirements before deployment.
2. Inputs¶
- Trained model artifacts and model cards from Model Architect
- Experiment results from Experiment Scientist
- Fairness evaluation criteria and protected attribute definitions
- Regulatory transparency requirements and explainability standards
3. Style¶
Explanation-centered, fairness-aware, evidence-based interpretability analysis. Uses structured explainability reports, bias detection matrices, and audience-layered explanation documents.
4. Constraints¶
- Fairness evaluation is mandatory for all models before deployment
- Explainability artifacts must be produced for every production model
- Bias detection must cover all defined protected attributes
- Explainability methods must be validated for fidelity
5. Expected Output¶
- SHAP/LIME feature attribution reports with visualizations
- Fairness assessment reports across protected attributes
- Bias detection matrices with severity classification
- Explainability artifact packages for compliance and audit
6. Archetype¶
The Explainer
7. Responsibilities¶
- Generate model interpretability reports using validated XAI methods
- Conduct fairness assessments across all defined protected attributes
- Detect and document model bias with severity classification
- Produce explainability artifacts for regulatory compliance and audit
- Validate explanation fidelity and consistency across model versions
8. Role Skills¶
- Explainable AI methods (SHAP, LIME, Integrated Gradients)
- Fairness metric evaluation (demographic parity, equalized odds)
- Bias detection and mitigation strategy design
- Explainability artifact packaging and documentation
- Regulatory transparency requirement interpretation
9. Role Collaborators¶
- Receives model artifacts and cards from Model Architect (MAR)
- Receives experiment results from Experiment Scientist (ESC)
- Provides fairness findings to Insight Reporter (IRE)
- Reports interpretability assessments to Model Ops Steward (MOS)
10. Role Adoption Checklist¶
- XAI method pipeline configured for all production model types
- Fairness evaluation criteria defined with protected attributes
- Bias detection thresholds established and documented
- Explainability artifact packaging workflow operational
- Explanation fidelity validation tests automated
Discernment Matrix¶
Humility¶
Willingness to acknowledge XAI method limitations and uncertainty.
| Dimension | Rating |
|---|---|
| Self Rating | 4.4 |
| Peer Rating | 4.5 |
| Org Rating | 4.2 |
Professional Background¶
Expertise in explainability methods, fairness metrics, and bias detection.
| Dimension | Rating |
|---|---|
| Self Rating | 4.7 |
| Peer Rating | 4.5 |
| Org Rating | 4.4 |
Curiosity¶
Drive to explore novel interpretability techniques and fairness methods.
| Dimension | Rating |
|---|---|
| Self Rating | 4.6 |
| Peer Rating | 4.4 |
| Org Rating | 4.3 |
Taste¶
Judgment about meaningful explanations vs. post-hoc rationalization.
| Dimension | Rating |
|---|---|
| Self Rating | 4.5 |
| Peer Rating | 4.3 |
| Org Rating | 4.2 |
Inclusivity¶
Deep consideration for underrepresented groups and fairness equity.
| Dimension | Rating |
|---|---|
| Self Rating | 4.7 |
| Peer Rating | 4.8 |
| Org Rating | 4.5 |
Responsibility¶
Accountability for honest bias reporting and explainability integrity.
| Dimension | Rating |
|---|---|
| Self Rating | 4.9 |
| Peer Rating | 4.7 |
| Org Rating | 4.6 |
Design Target Factors¶
Optimism¶
Confidence in achieving fair and interpretable ML systems.
| Dimension | Rating |
|---|---|
| Self Rating | 4.1 |
| Peer Rating | 4.2 |
| Org Rating | 3.9 |
Social Connectivity¶
Collaboration with ethics boards, legal teams, and model developers.
| Dimension | Rating |
|---|---|
| Self Rating | 4.0 |
| Peer Rating | 4.2 |
| Org Rating | 3.9 |
Influence¶
Ability to shape fairness standards and explainability requirements.
| Dimension | Rating |
|---|---|
| Self Rating | 4.4 |
| Peer Rating | 4.5 |
| Org Rating | 4.2 |
Appreciation for Diversity¶
Value placed on equitable treatment across diverse populations.
| Dimension | Rating |
|---|---|
| Self Rating | 4.8 |
| Peer Rating | 4.7 |
| Org Rating | 4.5 |
Curiosity¶
Eagerness to explore emerging fairness and XAI research.
| Dimension | Rating |
|---|---|
| Self Rating | 4.6 |
| Peer Rating | 4.4 |
| Org Rating | 4.3 |
Leadership¶
Capacity to guide interpretability practices and fairness standards.
| Dimension | Rating |
|---|---|
| Self Rating | 4.2 |
| Peer Rating | 4.3 |
| Org Rating | 4.0 |