AIF360 v0.3.0rc0 Release Notes

This is a major release containing a number of new features, improvements, and bugfixes.

Highlights

scikit-learn compatible API for certain algorithms, metrics, and datasets
Documentation layout was revamped to make it easier to navigate.
New algorithm:
- Fairness Gerrymandering (Kearns, et al., 2018)
New metrics:
- Differential Fairness (Foulds, et al., 2018)
- Rich Subgroup Fairness (Kearns, et al., 2018)

Optional dependencies may now be installed using the setuptools "extras" option: e.g., pip install 'aif360[LFR,AdversarialDebiasing]' or pip install 'aif360[all]'
Added support for integrations with MLOps (Kubeflow and NiFi) and examples
Added scores output to AdversarialDebiasing.predict() (#139)
Added a subset() method to StructuredDataset (#140)
scikit-learn compatibility (#134)
- EXPERIMENTAL: incomplete, contributions welcome
- 4 datasets (Adult, German, Bank, Compas) in DataFrame format with protected attributes in the index
  - Automatically downloads from openml.org
- 6 group fairness metrics as functions (statistical_parity_difference, disparate_impact_ratio, equal_opportunity_difference, average_odds_difference, average_odds_error, between_group_generalized_entropy_error)
- 2 individual fairness metrics as functions (generalized_entropy_index and its variants, consistency_score)
- 5 additional metrics as functions (specificity_score, base_rate, selection_rate, generalized_fpr, generalized_fnr)
- 3 algorithms (Reweighing, AdversarialDebiasing, CalibratedEqualizedOdds)

Fixed deprecation warning/NotImplementedError in StandardDataset (#115)
Fixed age threshold in GermanDataset (#129 and #137)
Corrected privileged/unprivileged attribute values for COMPAS dataset in some demos (#138)
Multiple documentation fixes (#114, #124, #153, #155, #157, #158, #159)