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

1. Policy: Define ethical principles and boundaries
2. Process: Review and approval workflows
3. People: Roles and responsibilities (ethics board)
4. 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