cloudthinker-ai AI Ethics Review
Phase 1: System Assessment
- Document the AI system
- System purpose and intended use
- Decision types (advisory, automated, human-in-the-loop)
- Affected stakeholders and populations
- Data sources and training data composition
- Model type and architecture
- Deployment context and scale
- Current safeguards and controls
- Classify system risk level
Risk Classification
| Factor | Low Risk | Medium Risk | High Risk | Assessment |
|---|---|---|---|---|
| Decision impact | Informational | Affects service quality | Affects rights/safety | |
| Reversibility | Easily reversed | Effort to reverse | Irreversible | |
| Affected population | Small, opt-in | Large, optional | Vulnerable, mandatory | |
| Autonomy level | Human decides | Human reviews | Fully automated | |
| Overall Risk |
Phase 2: Fairness Assessment
- 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
- Document bias risks and mitigations
Fairness Metrics
| Protected Group | Sample Size | Positive Rate | False Positive Rate | False Negative Rate | Disparity |
|---|---|---|---|---|---|
| Group A (reference) | % | % | % | N/A | |
| Group B | % | % | % | ratio | |
| Group C | % | % | % | ratio |
Phase 3: Transparency & Explainability
- 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
- 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
- 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
- Evaluate privacy-preserving techniques used
Phase 5: Safety & Robustness
- 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
- Evaluate potential for harm
Harm Assessment
| Potential Harm | Likelihood | Severity | Affected Group | Mitigation | Residual Risk |
|---|---|---|---|---|---|
| Low/Med/High | Low/Med/High | Low/Med/High |
Phase 6: Accountability & Governance
- 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
- Define ongoing monitoring and review cadence
Counter-Rationalizations
| Shortcut | Counter | Why |
|---|---|---|
| "We can skip some steps for this case" | Adapt the workflow steps, don't skip them | Skipped steps are where incidents and oversights originate |
| "The user seems to already know what to do" | Complete all workflow phases with the user | The workflow catches blind spots that experience alone misses |
| "This is a minor case, full process is overkill" | Scale the process down, don't turn it off | Minor cases become major when unstructured; the process scales, not disappears |
| "I'll fill in the details later" | Complete each section before moving on | Deferred details are forgotten; real-time capture is more accurate |
| "The template output isn't necessary" | Always produce the structured output format | Structured output enables comparison, audit trails, and handoff to other teams |
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)