This repository is the official implementation of the paper "Gradual (In)Compatibility of Fairness Criteria" by Corinna Hertweck and Tim Räz (available on arXiv). The paper explores the gradual (in)compatibility of the fairness metrics independence, separation and sufficiency.
You should have Python (ideally Anaconda) installed. The code has been built and tested with Python 3.7.9 with Anaconda 4.10.3.
If you use conda, you can create a virtual environment from the environment.yml file. The name of the environment will be gradual-compatibility. Notice, however, that the package pyitlib (which is used to evaluate the information-theoretic terms) cannot be installed through conda, so we have to additionally install it through pip.
conda env create -f environment.yml
conda activate gradual-compatibility
pip install pyitlib
If you do not have conda installed, you may also install the required packages through pip:
pip install -r requirements.txt
We recommend setting up a virtual environment for these dependencies.
The datasets can be found in the folder /data and are taken from Friedler et al. (2019).
update_compas_csv.py in the folder /data is used to preprocess the COMPAS data: It removes the columns that are the result of the one-hot encoding of the column c_charge_desc (which describes the charges raised against the defendant). This removal considerably speeds up training.
To select the lambda parameter for the L2 regularization of each dataset, run the notebook L2 - regularization.ipynb.
To evaluate the different regularizers in the paper, run these commands:
python evaluate_german.py
python evaluate_compas.py
python evaluate_adult.py
The results of these evalutions are saved in the folder /evaluations.
To plot the figures shown in the paper Plots - direct and indirect effects.ipynb. Edit the notebook to add or remove figures. In addition to being shown in the notebook, the figures are saved (in higher resolution) in the folder /images.