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 | | ...