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