This repository contains code for our AISTATS 2022 paper Pairwise Fairness for Ordinal Regression.
In order to reproduce the experiments of Section 5.1 of the paper, do the following:
-
install all required packages as specified in
requirements.txt
-
run
prepare_drug_consumption.py
andprepare_communities_and_crime.py
in order to download and prepare the data sets -
set the parameters at the top of
run_experiment.py
and run the script in order to produce plots like in Fig. 1 of our paper
-
The predictions produced by the POM model trained in Matlab are provided in Results/Predictions_MATLAB. If you have Matlab on your system, you can generate these on your own by running
run_POM_model_MATLAB.m
. -
In our paper, we present results averaged over a large number of splits and for a large number of different parameters --- creating these results is quite time consuming and we did so by running our code on numerous machines in parallel. The code provided here is intended to be run on a single machine, and we recommend to run it with parameters different from the ones that we used (see the top of
run_experiment.py
). Note that this will result in slightly worse results than those reported in our paper.
If you publish material that uses this code, please cite our paper:
@inproceedings{kleindessner2022fairordreg,
title={Pairwise Fairness for Ordinal Regression},
author={Kleindessner, Matthäus and Samadi, Samira and Zafar, Muhammad Bilal and Kenthapadi, Krishnaram and Russell, Chris},
year={2022},
booktitle={International Conference on Artificial Intelligence and Statistics (AISTATS)}
}
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.