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 methodology - •
references/regulatory_compliance.md- AI regulation requirements
Integration with Other Skills
- •machine-learning - For model development
- •testing - For bias testing
- •documentation - For model cards