Machine learning models can inadvertently inherit biases from training data, leading to unfair outcomes. This tutorial walks you through implementing fairness-aware techniques to mitigate such risks.
Key Steps to Ensure Fairness
Data Preprocessing
- Audit datasets for representation bias (e.g., imbalanced demographics)
- Use reweighing techniques to balance sensitive attributes
- Apply disparate impact analysis with tools like Fairlearn
Model Training
- Incorporate fairness constraints during training
- Example: Use equal opportunity or ** demographic parity** as objectives
- Try ** adversarial debiasing** methods for robustness
Fairness Metrics Evaluation
- Measure disparate impact using statistical tests
- Calculate equalized odds with AI Fairness 360 toolkit
- Visualize bias patterns with plots like Fairness_Diagram
Post-Processing Adjustments
- Apply calibration to equalize prediction rates
- Use threshold optimization for different groups
- Monitor for reinforcement of biases in production
Tools & Libraries
- Fairlearn for fairness-aware ML
- AI Fairness 360 for comprehensive fairness analysis
- Google's What-If Tool for interactive fairness audits
Best Practices 📌
✅ Always validate fairness metrics alongside accuracy
⚠️ Avoid over-reliance on a single fairness criterion
🔄 Regularly update bias detection protocols with new data
For deeper insights, explore our fairness-ml-concepts tutorial to understand core principles.