SLIDE

Official pytorch implementation of "SLIDE: A surrogate fairness constraint to ensure fairness consistency" published in Neural Networks (Volume 154, 2022, Pages 441-454) by Kunwoong Kim, Ilsang Ohn, Sara Kim, and Yongdai Kim.

Usage

1. Locate your custom_dataset in the directory "datasets/{custom_dataset}".

2. Add loading function in "load.data_py" that should returns a tuple (xs, x, y, s) consisting four torch.tensor where xs = torch.cat([x, s.reshape(s.size(0), 1)], dim=1).

3. run SLIDE as the command: "python main.py --dataset {custom_dataset} --lmda {lmda}" where lmda is the fairness hyper-parameter, higher lmda increases the level of fairness (demographic parity or disparate impact).

For example, you can command

python main.py --dataset law --lmda 5.0

Environments

These codes are based on the following environments and versions of the corresponding libraries.

python >= 3.6 torch >= 1.8.0