This repository contains code for constructing and evaluating unbiased maximum-entropy distributions from biased datasets.
The goal of constructing such distributions is to debias data from real-world sources. In this repository, examples of evaluation with respect to Compas recidivism data and Adult income data are given. The package requirements are provided in the requirements.txt file.
The files FairMaxEnt-expts-1.ipynb and FairMaxEnt-expts-2.ipynb provide a detailed examples for the usage of the max-entropy framework to debias real-world datasets.
The evaluation with respect to Compas dataset is divided into two parts.
The file FairMaxEnt-expts-1.ipynb provides evaluation code for the first and the file FairMaxEnt-expts-2.ipynb provides evaluation code for the second.
For Adult dataset (504 elements in domain), just replace the dataset and domain loading function in FairMaxEnt-expts-1.ipynb file.
The folder FairMaxEnt contains the backend code for constructing max-entropy distributions for any given set of parameters, and can be used for more general applications as well.
Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi
International Conference on Machine Learning, ICML 2020
Please cite the corresponding paper when using the code