Quarterly AI Ethics Audit Checklist
Pre-formatted checklist for regular ethical monitoring of AI systems in Monitoring & Evaluation
Quarter: | Assessment Period:
1. Bias & Fairness Monitoring
Evaluate ongoing bias risks and fairness metrics across demographic subgroups
Performance Disparity Analysis
CRITICALCheck model accuracy across all demographic groups (gender, ethnicity, geography, income, disability). Maximum allowed variance: 10%
Document Performance Metrics:
Intersectional Bias Testing
Test model performance across intersectional subgroups (e.g., rural women with disabilities)
Historical Bias Review
Analyze whether historical discrimination patterns are being replicated in AI predictions
2. Privacy & Data Protection
Review data handling practices, consent mechanisms, and compliance with privacy regulations
Data Privacy Compliance
CRITICALVerify compliance with GDPR, local data protection laws, and organizational privacy policies
Consent Mechanism Review
Review and update informed consent mechanisms for data collection and AI processing
Data Anonymization Check
Verify effectiveness of data anonymization and pseudonymization techniques
3. Transparency & Accountability
Evaluate documentation, explainability, and decision-making processes
Documentation Update
Update all system documentation including data sources, algorithms, and limitations
Explainability Review
Test that AI decisions can be explained to non-technical stakeholders
Human Oversight Verification
CRITICALVerify human-in-the-loop mechanisms and override capabilities are functional
4. Environmental Impact Assessment
Monitor carbon footprint, energy consumption, and sustainability practices
Carbon Footprint Calculation
Calculate and document CO2 emissions from AI model training and inference
Energy Efficiency Review
Review infrastructure choices and optimization opportunities
5. Security & System Robustness
Evaluate security measures, resilience, and failure recovery capabilities
Security Vulnerability Scan
CRITICALConduct security assessment for potential vulnerabilities and attack vectors
Drift Detection Review
Review model drift detection mechanisms and alerting systems
6. Stakeholder Engagement & Feedback
Review community consultation processes and grievance mechanisms
Stakeholder Consultation Review
Document stakeholder feedback sessions and consultation outcomes
Grievance Mechanism Audit
Review and test complaint resolution processes and accessibility
7. Regulatory & Governance Compliance
Verify compliance with international frameworks and organizational policies
EU AI Act Compliance
Review compliance with EU AI Act requirements based on risk classification
UNESCO Principles Alignment
Verify alignment with UNESCO Recommendation on AI Ethics
📝 Audit Notes & Observations
🚀 Recommended Corrective Actions
Based on audit findings, the following actions are recommended:
📚 Related Resources & Templates
Bias Audit Framework
Comprehensive framework for auditing algorithmic bias in program evaluations
View Framework →7-Core-Risk Assessment
Complete risk assessment template for AI systems in M&E contexts
Use Assessment Tool →EvalCommunity Academy. This interactive quarterly audit checklist is part of the AI in Monitoring & Evaluation curriculum.
Recommended audit frequency: Quarterly | Next audit due: