AI Quality Assurance Framework

1. Governance & Foundations

This is the backbone of the entire QA strategy.

Objectives

• Establish accountability
• Define quality standards
• Ensure compliance and ethical alignment

Key Components

• AI Governance Board Oversees risk, approves models, sets policies.
• AI Risk Classification Categorise each AI system as:
  • Low risk
  • Medium risk
  • High risk
  • Critical (healthcare, finance, safety, legal decisions)
• Quality Standards Definition Define what “good” means for your system:
  • Accuracy
  • Fairness
  • Explainability
  • Robustness
  • Safety
  • Security
  • Reliability

Deliverables

• AI Policy
• AI Risk Register
• Model Approval Checklist
• Ethical Impact Assessment

2. Data Quality Assurance

Data is the foundation of AI performance.

Objectives

Ensure training, validation, and production data are clean, representative, and unbiased.

Testing Areas

• Data completeness
• Data consistency
• Bias detection
• Label accuracy
• Outlier detection
• Data lineage tracking

Tools & Methods

• Statistical profiling
• Bias audits
• Synthetic data tests
• Data versioning (DVC, LakeFS)

Deliverables

• Data Quality Report
• Bias & Fairness Audit
• Data Documentation Sheet

3. Model Quality Assurance

This is where you test the model itself.

Objectives

Validate the model’s performance, robustness, and ethical behaviour.

Testing Areas

• Performance metrics Accuracy, precision, recall, F1, ROC-AUC, BLEU, etc.
• Robustness testing
  • Edge cases
  • Noisy inputs
  • Adversarial examples
• Fairness testing
  • Group fairness
  • Individual fairness
  • Disparate impact
• Explainability testing
  • SHAP
  • LIME
  • Counterfactual explanations
• Stress testing
  • Extreme inputs
  • High‑volume requests

Deliverables

• Model Evaluation Report
• Fairness & Bias Report
• Explainability Report
• Model Card

4. System Integration & Functional QA

AI doesn’t live alone — it lives inside a system.

Objectives

Ensure the AI works correctly in the real application.

Testing Areas

• API behaviour
• Latency & throughput
• Error handling
• Model versioning
• Security testing
  • Prompt injection
  • Adversarial attacks
  • Data leakage
• Fail‑safe behaviour
  • Fallback responses
  • Human escalation

Deliverables

• Integration Test Suite
• Security Test Report
• Fail‑Safe Design Document

5. Human‑in‑the‑Loop (HITL) QA

Humans remain essential for high‑risk or ambiguous decisions.

Objectives

Ensure humans can override, correct, and improve the AI.

Components

• Human review workflows
• Escalation paths
• Confidence thresholds
• Annotation pipelines
• Feedback loops

Deliverables

• HITL Workflow Diagram
• Reviewer Guidelines
• Feedback Logging System

6. Deployment QA

Before going live, you run a final validation.

Objectives

Ensure the model is safe, stable, and ready for production.

Checklist

• Performance validated
• Bias within acceptable limits
• Security tested
• Monitoring configured
• Rollback plan ready
• Stakeholders sign‑off

Deliverables

• Go‑Live Readiness Report
• Deployment Checklist
• Rollback Plan

7. Continuous Monitoring & Drift Detection

AI degrades over time — this phase keeps it healthy.

Objectives

Detect performance drops, bias drift, or unexpected behaviour.

Monitoring Areas

• Data drift
• Concept drift
• Model performance decay
• User feedback trends
• Anomaly detection
• Safety monitoring

Deliverables

• Monitoring Dashboard
• Monthly Drift Report
• Incident Log
• Retraining Schedule

8. Continuous Improvement & Lifecycle Management

AI QA is never “done.”

Objectives

Ensure the model evolves safely and effectively.

Activities

• Scheduled retraining
• Re‑evaluation of fairness
• Updating documentation
• Re‑certification for high‑risk models
• Post‑incident reviews

Deliverables

• Model Lifecycle Plan
• Retraining Documentation
• Updated Model Card