v0.1.0 release

v0.1.0

Intersectional Fairness 0.1.0 Release Notes

---

Description

Intersectional Fairness (ISF) is a bias detection and mitigation technology for intersectional bias, which combinations of multiple protected attributes causes.

ISF leverages the existing single-attribute bias mitigation methods to make a machine-learning model fair regarding intersectional bias.

Approaches applicable to ISF are pre-, in-, and post-processing. For now, ISF supports Adversarial Debiasing, Equalized Odds, Massaging, and Reject Option Classification.

Highlights

• Python 3.7, AIF360 0.4 now supported
• Datasets Interface
  • table data(Structured Dataset)
• Bias mitigation Algorithms
  • Adversarial Debiasing
  • Equalized Odds
  • Massaging
  • Reject Option Classification
• Fairness metrics
  • Demographic Parity
  • Equal Opportunity
  • Equalized Odds
  • F1Parity
• Accuracy metrics
  • Balanced Accuracy
  • F1
• Related techniques used in tutorials
  • Logistic Regression
  • Disparate Impact