Fairness metrics are essential tools for evaluating and mitigating bias in machine learning models. They help quantify disparities in outcomes across different protected attributes (e.g., gender, race, age) and ensure equitable treatment of all groups. Below are key concepts and applications:
🔍 Key Concepts
- Disparate Impact: Measures whether a model’s predictions disproportionately affect certain groups.
- Example: A hiring algorithm that rejects more female applicants than male ones.
- Statistical Parity: Ensures equal true positive rates across all groups.
- Formula: $ \frac{TP_1}{P_1} = \frac{TP_2}{P_2} $
- Equal Opportunity: Focuses on equal false negative rates for sensitive attributes.
- Use case: Loan approval systems avoiding discrimination against minority applicants.
📊 Common Metrics
Metric | Description |
---|---|
Accuracy Disparity | Difference in model accuracy between groups. |
Predictive Parity | Equal precision across all groups. |
F1 Score Disparity | Unequal balance between precision and recall for different groups. |
🧠 Applications
- Healthcare: Ensuring fair diagnosis outcomes across demographics.
- Criminal Justice: Reducing bias in risk assessment tools.
- Finance: Equitable credit scoring for all income levels.
For deeper insights, explore our tutorial on ML Fundamentals or AI Ethics.