ai-ethics-review

name	ai-ethics-review
enabled	true
description	Use when performing ai ethics review — conducts an ethical review of AI/ML systems covering fairness, transparency, accountability, privacy, and safety. Evaluates potential harms, bias in training data and model outputs, explainability requirements, and produces an ethics impact assessment with mitigation recommendations.
required_connections	[{"prefix":"ml-platform","label":"ML Platform"}]
config_fields	[{"key":"system_name","label":"AI System Name","required":true,"placeholder":"e.g., Loan Approval Model, Content Recommendation Engine"},{"key":"use_case","label":"Primary Use Case","required":true,"placeholder":"e.g., automated decision-making for credit applications"},{"key":"affected_population","label":"Affected Population","required":false,"placeholder":"e.g., loan applicants, job candidates, content consumers"}]
features	["DATA","AI","ETHICS"]

AI Ethics Review

Phase 1: System Assessment

1. 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
2. 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

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

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

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

Potential Harm	Likelihood	Severity	Affected Group	Mitigation	Residual Risk
	Low/Med/High	Low/Med/High			Low/Med/High

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

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)