aiskillstore/ai-ethics

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