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Versions

  • v0.4.6: Documentation page not present
  • v0.5.0
  • v0.6.0
  • main
  • Fairness in Machine Learning
  • Assessment
  • Mitigation
  • Migrating from prior versions
  • Further Resources

User GuideΒΆ

  • 1. Fairness in Machine Learning
    • 1.1. Fairness of AI systems
    • 1.2. Types of harms
    • 1.3. Fairness assessment and unfairness mitigation
      • 1.3.1. Group fairness, sensitive features
      • 1.3.2. Parity constraints
      • 1.3.3. Disparity metrics, group metrics
  • 2. Assessment
    • 2.1. Metrics
      • 2.1.1. Ungrouped Metrics
      • 2.1.2. Metrics with Grouping
      • 2.1.3. Scalar Results from MetricFrame
      • 2.1.4. Control features for grouped metrics
    • 2.2. Fairlearn dashboard
      • 2.2.1. Setup and a single-model assessment
      • 2.2.2. Comparing multiple models
  • 3. Mitigation
    • 3.1. Postprocessing
    • 3.2. Reductions
      • 3.2.1. Fairness constraints for binary classification
      • 3.2.2. Fairness constraints for multi-class classification
      • 3.2.3. Fairness constraints for regression
      • 3.2.4. Exponentiated Gradient
      • 3.2.5. Grid Search
  • 4. Migrating from prior versions
    • 4.1. Migrating to v0.5.0 from v0.4.6
      • 4.1.1. Metrics
      • 4.1.2. Renamed object attributes
      • 4.1.3. Exponentiated Gradient and Moments
  • 5. Further Resources
    • 5.1. Books
    • 5.2. Papers
    • 5.3. Online demos, talks, tutorials

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