Agent skill
ai-ethics
Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.
Install this agent skill to your Project
npx add-skill https://github.com/89jobrien/steve/tree/main/steve/skills/ai-ethics
SKILL.md
AI Ethics
Comprehensive AI ethics skill covering bias detection, fairness assessment, responsible AI development, and regulatory compliance.
When to Use This Skill
- Evaluating AI models for bias
- Implementing fairness measures
- Conducting ethical impact assessments
- Ensuring regulatory compliance (EU AI Act, etc.)
- Designing human-in-the-loop systems
- Creating AI transparency documentation
- Developing AI governance frameworks
Ethical Principles
Core AI Ethics Principles
| Principle | Description |
|---|---|
| Fairness | AI should not discriminate against individuals or groups |
| Transparency | AI decisions should be explainable |
| Privacy | Personal data must be protected |
| Accountability | Clear responsibility for AI outcomes |
| Safety | AI should not cause harm |
| Human Agency | Humans should maintain control |
Stakeholder Considerations
- Users: How does this affect people using the system?
- Subjects: How does this affect people the AI makes decisions about?
- Society: What are broader societal implications?
- Environment: What is the environmental impact?
Bias Detection & Mitigation
Types of AI Bias
| Bias Type | Source | Example |
|---|---|---|
| Historical | Training data reflects past discrimination | Hiring models favoring male candidates |
| Representation | Underrepresented groups in training data | Face recognition failing on darker skin |
| Measurement | Proxy variables for protected attributes | ZIP code correlating with race |
| Aggregation | One model for diverse populations | Medical model trained only on one ethnicity |
| Evaluation | Biased evaluation metrics | Accuracy hiding disparate impact |
Fairness Metrics
Group Fairness:
- Demographic Parity: Equal positive rates across groups
- Equalized Odds: Equal TPR and FPR across groups
- Predictive Parity: Equal precision across groups
Individual Fairness:
- Similar individuals should receive similar predictions
- Counterfactual fairness: Would outcome change if protected attribute differed?
Bias Mitigation Strategies
Pre-processing:
- Resampling/reweighting training data
- Removing biased features
- Data augmentation for underrepresented groups
In-processing:
- Fairness constraints in loss function
- Adversarial debiasing
- Fair representation learning
Post-processing:
- Threshold adjustment per group
- Calibration
- Reject option classification
Explainability & Transparency
Explanation Types
| Type | Audience | Purpose |
|---|---|---|
| Global | Developers | Understand overall model behavior |
| Local | End users | Explain specific decisions |
| Counterfactual | Affected parties | What would need to change for different outcome |
Explainability Techniques
- SHAP: Feature importance values
- LIME: Local interpretable explanations
- Attention maps: For neural networks
- Decision trees: Inherently interpretable
- Feature importance: Global model understanding
Model Cards
Document for each model:
- Model purpose and intended use
- Training data description
- Performance metrics by subgroup
- Limitations and ethical considerations
- Version and update history
AI Governance
AI Risk Assessment
Risk Categories (EU AI Act):
| Risk Level | Examples | Requirements |
|---|---|---|
| Unacceptable | Social scoring, manipulation | Prohibited |
| High | Healthcare, employment, credit | Strict requirements |
| Limited | Chatbots | Transparency obligations |
| Minimal | Spam filters | No requirements |
Governance Framework
- Policy: Define ethical principles and boundaries
- Process: Review and approval workflows
- People: Roles and responsibilities (ethics board)
- Technology: Tools for monitoring and enforcement
Documentation Requirements
- Data provenance and lineage
- Model training documentation
- Testing and validation results
- Deployment and monitoring plans
- Incident response procedures
Human Oversight
Human-in-the-Loop Patterns
| Pattern | Use Case | Example |
|---|---|---|
| Human-in-the-Loop | High-stakes decisions | Medical diagnosis confirmation |
| Human-on-the-Loop | Monitoring with intervention | Content moderation escalation |
| Human-out-of-Loop | Low-risk, high-volume | Spam filtering |
Designing for Human Control
- Clear escalation paths
- Override capabilities
- Confidence thresholds for automation
- Audit trails
- Feedback mechanisms
Privacy Considerations
Data Minimization
- Collect only necessary data
- Anonymize when possible
- Aggregate rather than individual data
- Delete data when no longer needed
Privacy-Preserving Techniques
- Differential privacy
- Federated learning
- Secure multi-party computation
- Homomorphic encryption
Environmental Impact
Considerations
- Training compute requirements
- Inference energy consumption
- Hardware lifecycle
- Data center energy sources
Mitigation
- Efficient architectures
- Model distillation
- Transfer learning
- Green hosting providers
Reference Files
references/bias_assessment.md- Detailed bias evaluation methodologyreferences/regulatory_compliance.md- AI regulation requirements
Integration with Other Skills
- machine-learning - For model development
- testing - For bias testing
- documentation - For model cards
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