ai-ethics

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Translated

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.

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Source89jobrien/steve

Added on2026-02-26

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SKILL.md Content

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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