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

ISF leverages existing single-attribute bias mitigation methods to ensure fairness in machine-learning models when it comes to intersectional bias.

The approaches applicable to ISF include pre-, in-, and post-processing. Currently, ISF supports Adversarial Debiasing, Equalized Odds, Massaging, and Reject Option Classification.

Intersectional Fairness (ISF) is a sandbox-stage project of LF AI & Data Foundation.

Contributed by Fujitsu Limited in June 2023.