Agent skill · research science · aiskillstore
ai-ethics
Responsible AI development and ethical considerations. Use when evaluating
Why this skill is useful
Provides domain-specific knowledge on AI ethics, bias detection, and fairness metrics that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 Remo …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ai-ethics