ai-ethics
Implement ethical AI practices and responsible AI governance. Use for: identifying and mitigating bias in datasets and models, ensuring fairness across demographic groups, implementing transparency and explainability requirements, protecting user privacy and data security, establishing accountability frameworks, conducting ethical impact assessments, complying with AI regulations (GDPR, EU AI Act), implementing human oversight mechanisms, and building trustworthy AI systems aligned with societal values.
npx skills add majiayu000/claude-skill-registry --skill ai-ethics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AI Ethics Build responsible, fair, and trustworthy AI systems that respect human rights and societal values. ## Overview AI ethics addresses moral questions and societal impacts of artificial intelligence systems. As AI increasingly influences critical decisions in healthcare, finance, criminal justice, and employment, ensuring ethical development and deployment is paramount. This skill covers fairness, transparency, accountability, privacy, safety, and governance frameworks for responsible AI. ## Core Ethical Principles ### Fairness and Non-Discrimination Ensure AI systems treat all individuals and groups equitably. **Key Concepts:** - **Individual Fairness**: Similar individuals receive similar outcomes - **Group Fairness**: Equal outcomes across demographic groups - **Equalized Odds**: Equal true positive and false positive rates across groups - **Demographic Parity**: Equal prediction rates across groups ```python from aif360.datasets import BinaryLabelDataset from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric from aif360.algorithms.preprocessing import Reweighing # Load dataset with protected attributes dataset = BinaryLabelDataset( df=df, label_names=
- Overview
- Core Ethical Principles
- Fairness and Non-Discrimination
- Transparency and Explainability
- Accountability
- Privacy and Data Protection
- Bias Detection and Mitigation
- Identifying Bias Sources
- Bias Mitigation Techniques
- Fairness Metrics
- AI Governance Frameworks
- OECD AI Principles
- EU AI Act Risk Classification
- NIST AI Risk Management Framework
What does the ai-ethics skill do?
Implement ethical AI practices and responsible AI governance. Use for: identifying and mitigating bias in datasets and models, ensuring fairness across demographic groups, implementing transparency and explainability requirements, protecting user privacy and data security, establishing accountability frameworks, conducting ethical impact assessments, complying with AI regulations (GDPR, EU AI Act), implementing human oversight mechanisms, and building trustworthy AI systems aligned with societal values.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-ethics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.