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