Model Bias and Fairness
Identifying, measuring, and mitigating algorithmic bias to ensure equitable outcomes in AI systems.
npx skills add majiayu000/claude-skill-registry --skill model-bias-fairness-amnadtaowsoam-cerebratechai-claude-2 --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.
# Model Bias and Fairness ## Overview Model Bias occurs when an AI system produces results that are systematically prejudiced against certain individuals or groups. Fairness is the practice of ensuring that the model's predictions do not vary unfairly across protected attributes (e.g., race, gender, age). **Core Principle**: "Bias is a feature of data, fairness is a requirement of the system." --- ## 1. Types of Algorithmic Bias | Bias Type | Description | Example | | :--- | :--- | :--- | | **Historical Bias** | Pre-existing prejudice in the world. | Credit scoring models reflecting historical redlining. | | **Representation Bias**| Underrepresentation of certain groups in training data. | Facial recognition failing on darker skin tones. | | **Measurement Bias** | Issues with how data is collected or labeled. | Using "Arrests" as a proxy for "Crime" when certain areas are over-policed. | | **Algorithmic Bias** | The math itself favors a certain outcome. | Maximizing "Total Revenue" might favor high-income zip codes unfairly. | --- ## 2. Quantitative Fairness Metrics You cannot manage what you do not measure. | Metric | Goal | Equation | | :--- | :--- | :--- | | **Demographic Parity
- Overview
- 1. Types of Algorithmic Bias
- 2. Quantitative Fairness Metrics
- 3. Mitigation Strategies
- A. Pre-processing (Data Level)
- B. In-processing (Model Level)
- C. Post-processing (Prediction Level)
- 4. Implementation with Fairlearn (Python)
- Measuring Disparity
- Mitigating Bias (Threshold Optimization)
- 5. The Fairness Audit Workflow
- 6. Tools Landscape
- 7. Compliance: EU AI Act & NIST
- 8. Real-World Scenario: The Recruitment Filter
What does the Model Bias and Fairness skill do?
Identifying, measuring, and mitigating algorithmic bias to ensure equitable outcomes in AI systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-bias-fairness-amnadtaowsoam-cerebratechai-claude-2 --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.