SOP-1302-01-AI_AI-Explainability-and-Model-Transparency
Effective Date: 2025-02-01
Previous Version: None
Owner: AI Governance Office
1. Purpose
This Standard Operating Procedure (SOP) defines the methods and standards for ensuring Explainability and Model Transparency throughout the AI Solution Development Life Cycle (AI-SDLC). It specifies the responsibilities, activities, and documentation requirements needed to maintain clarity on how AI models make decisions, thereby addressing both regulatory and ethical expectations.
2. Scope
Applies To:
All AI-SDLC projects where AI or ML solutions are developed, tested, or deployed.
Any third-party or open-source models integrated into our environment.
AI-IRB governance processes that require traceability of model decisions and interpretability.
Exclusions:
Purely experimental or research prototypes not going to production, unless specifically requested by AI-IRB.
3. References
SOP-1300-01-AI: AI-IRB Governance & Oversight
SOP-1301-01-AI: AI Bias & Fairness Evaluation
ISO/IEC TR 24028: Overview of trustworthiness in AI
EU AI Act (Draft) and other relevant local or regional laws
4. Definitions
Explainability: The degree to which an AI/ML model’s internal logic or predictions can be understood by human stakeholders.
Model Transparency: The openness and clarity with which a model’s architecture, parameters, and reasoning can be inspected.
Post-hoc Explanation Tools: Methods that analyze trained models to generate interpretability (e.g., LIME, SHAP).
AI-IRB: A regulatory-style board that reviews AI projects for compliance, ethics, fairness, bias, etc.
5. Roles & Responsibilities
| Role | Responsibility |
|---|---|
| AI Governance Office | Oversees creation and enforcement of explainability requirements; coordinates with AI-IRB. |
| Project Sponsor | Provides budget/resources to ensure model transparency measures are in place. |
| AI-IRB | Reviews and approves interpretability strategies for compliance with ethics/regulations. |
| Data Scientist/Engineer | Implements or selects explainability tools, configures model-interpretation approaches, and updates documentation. |
| Quality Assurance (QA) | Validates the completeness of explanation methods, ensures correctness of transparency reports. |
| Operations | Deploys solutions with integrated interpretability services and ensures continuity of explanation features. |
| Technical Support | Communicates explanation details to internal or external users, funnels feedback on interpretability issues to Data Science. |
6. Procedure Activities
6.1 Identify Explainability Requirements
Initiation: The AI Governance Office and Data Scientists meet during project planning to define the level of explanation needed (e.g., local vs. global interpretability).
Regulatory & Ethical Inputs: The AI-IRB provides specific compliance guidelines (e.g., thresholds for explanation clarity).
Requirement Documentation: Required interpretability metrics and scope are documented in the project’s scope of work (SOW).
6.2 Select Model Architecture & Explanation Approach
Initial Model Design: Data Scientists propose one or more model architectures.
Feasibility Check: Evaluate whether the model can incorporate built-in interpretability features or if external methods (e.g., LIME, SHAP) are necessary.
AI-IRB Review: Obtain AI-IRB concurrence if the proposed solution meets or surpasses mandatory transparency thresholds.
6.3 Implement and Validate Explanation Mechanisms
Development of Explanation Artifacts: Data Scientists implement selected approach (e.g., storing feature importances, local explanations).
Internal Testing:
Data Scientists perform tests verifying that explanations are consistent with model’s predictions.
QA reviews logs and explanation outputs to verify correctness.
Iteration: If QA or the AI-IRB finds the explanations insufficient, Data Scientists revise the approach.
6.4 Prepare Documentation & Communication Materials
Explainability Documentation: Data Scientists prepare user-friendly references about:
Explanation methods used
Explanatory limitations or disclaimers
User-Facing Summaries: Technical Support refines these materials for non-technical audiences.
Approval: The AI Governance Office and AI-IRB sign off on final documentation for readiness to deploy.
6.5 Deployment & Monitoring
Deployment: Operations deploy the model along with the integrated explanation components in staging, then production.
Explainability Monitoring:
The system logs usage of explanation features (requests, success, or failures).
The AI-IRB monitors metrics on user-satisfaction or fallback rates if explanations fail.
Incident Management: If explanation anomalies are reported (e.g., contradictory interpretation output), the Data Scientist investigates root cause.
6.6 Post-Implementation Review
Collect Feedback:
Technical Support gathers user feedback on transparency & clarity of explanations.
QA checks that stored logs conform to compliance.
Lessons Learned:
AI Governance Office organizes the Post-Implementation meeting to identify improvement areas.
Revisions to the SOP or practice are documented if needed.
7. Records & Documentation
Explainability Requirements Document
Implementation Approach & Tools
Explanation Testing Logs
Final Explanation Documentation
Post-Implementation Review Summary
8. Metrics
Explainability Coverage: Percentage of predictions that have explanations.
Turnaround Time: Speed at which explanation is generated.
User Satisfaction: Feedback from internal or external consumers of the explanations.
9. Revision History
| Version | Date | Changes | Approved By |
|---|---|---|---|
| 1.0 | 2025-02-01 | Initial Release of Document. | AI Governance Office |
Approval & Concurrence
AI Governance Office Director: Signature / Date
AI-IRB Chair: Signature / Date
Development Manager: Signature / Date
Quality Assurance Lead: Signature / Date
Prepared By:
AI Governance Office
Reviewed By:
AI-IRB, Quality Assurance, Data Science Team
End of SOP-1302-01-AI
@startuml
title SOP-1302-01-AI: AI Explainability & Model Transparency
’ Define participants (roles) with short names
participant “AI Gov Office” as GOV
participant “AI-IRB” as IRB
participant “Project Sponsor” as PS
participant “Data Sci/Eng” as DSC
participant “Quality Assurance” as QA
participant “Operations” as OPS
participant “Technical Support” as TS
’ 1. GOV and DSC define the level of explainability needed
GOV -> DSC: Discuss project scope & define explainability level
DSC -> GOV: Provide technical feasibility & outline approach
’ 2. IRB provides regulatory & ethical guidelines
GOV -> IRB: Request compliance guidelines
IRB -> GOV: Provide guidelines for transparency & interpretability
’ 3. DSC documents requirements
DSC -> DSC: Document interpretability metrics & scope
DSC -> PS: Present resource needs for implementing explanation
’ 4. DSC + IRB confirm approach meets or exceeds thresholds
DSC -> IRB: Submit proposed approach for interpretability
alt Approach accepted
IRB -> DSC: Concur on approach
else Approach not accepted
IRB -> DSC: Request modifications
DSC -> DSC: Update approach & resubmit
end
’ 5. DSC implements explanation mechanism
DSC -> DSC: Implement local/global explanation methods (LIME, SHAP, etc.)
’ 6. QA verifies correctness & logs
DSC -> QA: Provide explanation outputs for internal testing
QA -> QA: Validate explanation consistency with model
alt Explanation insufficient
QA -> DSC: Request fix or improvement
DSC -> DSC: Revise approach
QA -> QA: Retest
else Explanation sufficient
QA -> DSC: Approve QA test results
end
’ 7. DSC finalizes documentation
DSC -> DSC: Create user-friendly references for model interpretability
’ 8. TS refines user-facing content
DSC -> TS: Deliver technical docs
TS -> TS: Convert docs for non-technical audiences
’ 9. IRB & GOV sign-off on final documentation
TS -> IRB: Submit final interpretability docs
IRB -> GOV: Final compliance check
GOV -> TS: Approved to move forward
‘10. OPS deploys model & explanation features
OPS -> OPS: Deploy code & explanation modules to staging/production
‘11. Explanation usage is monitored
OPS -> GOV: Provide logs/metrics on explanation usage
‘12. If anomalies arise, DSC investigates root cause
alt Explanation anomalies
OPS -> DSC: Report contradictory interpretation logs
DSC -> DSC: Investigate root cause & fix
QA -> DSC: Validate the fix
end
‘13. Post-Implementation Review
GOV -> TS: Gather user feedback on explanation clarity
TS -> GOV: Provide compiled feedback
GOV -> IRB: Discuss lessons learned
IRB -> DSC: Potential procedure updates if needed
@enduml
Short textual explanation:
This sequence diagram illustrates the end-to-end process for ensuring AI model explainability and transparency. It starts with defining the interpretability requirements, receiving guidance from the AI-IRB, and implementing explanation methods. Quality Assurance verifies correctness, while the AI Governance Office and IRB approve final documentation. The model is deployed by Operations with integrated explanation features, monitored for anomalies, and subject to a post-implementation review for continuous improvement.