Fairlearn
An open-source, community-driven project to help data scientists improve the fairness of AI systems.
Overview
Fairlearn is an open-source Python package that empowers developers and data scientists to assess and improve the fairness of their machine learning models. It focuses on group fairness, which addresses which groups of individuals are at risk of being harmed by a model's predictions. Fairlearn provides tools to evaluate fairness and algorithms to mitigate unfairness, integrating with popular machine learning libraries.
✨ Key Features
- Fairness assessment dashboard for interactive visualization
- Unfairness mitigation algorithms (e.g., Exponentiated Gradient, Grid Search)
- Metrics for both classification and regression models
- Focus on sociotechnical aspects of fairness
- Integration with scikit-learn
🎯 Key Differentiators
- Interactive visualization dashboard for fairness assessment
- Strong focus on the sociotechnical aspects of fairness
- Seamless integration with the scikit-learn ecosystem
Unique Value: Provides an intuitive and interactive approach to assessing and mitigating group fairness issues in machine learning models.
🎯 Use Cases (4)
✅ Best For
- Fairness assessment in credit-card default models
- Evaluating and mitigating gender bias in hiring models
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Users requiring a purely no-code interface
- Applications that require individual fairness metrics beyond group fairness
🏆 Alternatives
Offers a more user-friendly and visually interactive experience for fairness assessment compared to some other open-source toolkits.
💻 Platforms
✅ Offline Mode Available
🔌 Integrations
💰 Pricing
Free tier: Fully open-source and free to use.
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