Ai Ethics Review

Phase 2: Fairness Assessment

1. Evaluate fairness and bias
   • Protected attributes identified (race, gender, age, disability)
   • Training data representation analyzed
   • Historical bias in training data assessed
   • Proxy variables for protected attributes identified
   • Fairness metrics defined (demographic parity, equalized odds, etc.)
   • Disparate impact analysis conducted
   • Subgroup performance compared
2. Document bias risks and mitigations

Fairness Metrics

Phase 3: Transparency & Explainability

1. Assess transparency requirements
   • Model decisions are explainable to affected individuals
   • Feature importance is documented and reasonable
   • Decision rationale can be provided on request
   • Model limitations clearly documented
   • Users informed they are interacting with AI
   • Training data sources disclosed
2. Evaluate explainability methods
   • Global explanations (feature importance, model summary)
   • Local explanations (individual decision rationale)
   • Counterfactual explanations ("what would need to change")

Phase 4: Privacy & Data Protection

1. Assess privacy risks
   • Consent obtained for data usage in AI training
   • Data minimization practiced (only necessary features)
   • Re-identification risk assessed for anonymized data
   • Model memorization risk evaluated
   • Data retention aligned with privacy policies
   • Right to be forgotten implementable
   • Cross-border data transfer compliance
2. Evaluate privacy-preserving techniques used

Phase 5: Safety & Robustness

1. Assess safety considerations
   • Failure modes identified and mitigated
   • Adversarial robustness tested
   • Out-of-distribution detection implemented
   • Human override capability exists
   • Escalation path for edge cases
   • Monitoring for model degradation
   • Kill switch for emergency shutdown
2. Evaluate potential for harm

Harm Assessment

Phase 6: Accountability & Governance

1. Assess accountability structures
   • Responsible party identified for AI system
   • Governance review board or ethics committee
   • Regular audit schedule established
   • Complaint/appeal mechanism for affected individuals
   • Documentation of decisions and rationale
   • Regulatory compliance verified (EU AI Act, etc.)
   • Model card published
2. Define ongoing monitoring and review cadence

Counter-Rationalizations

Output Format

• Ethics Impact Assessment: Comprehensive review across all dimensions
• Risk Classification: System risk level with justification
• Fairness Report: Bias analysis with demographic breakdowns
• Mitigation Plan: Identified risks with recommended actions
• Governance Framework: Ongoing oversight and accountability plan

Action Items

• Complete system documentation and risk classification
• Conduct fairness and bias analysis on model outputs
• Implement explainability methods appropriate to risk level
• Verify privacy compliance and data minimization
• Test robustness and document failure modes
• Establish accountability and governance structure
• Schedule recurring ethics review (minimum annually)