MinimaxFair - Convergent algorithms for (relaxed) minimax group fairness

MinimaxFair is a Python package for training ML models for (relaxed) minimax group fairness as discussed in Minimax group fairness: Algorithms and experiments.

This repository contains python code for

• learning models that achieve minimax group fairness for both regression and classification tasks
• learning models that minimize error subject to relaxed group fairness constraints
• visualizing tradeoffs between fairness and overall error

We also include some examples of fairness sensitive datasets for experimentation, though our package supports any dataset formatted as a .csv whose columns are labeled

Our algorithms support the following training objectives (loss functions):

• Mean Squared Error
• 0/1 Loss
• Log Loss
• False Positive Rate
• False Negative Rate

Our algorithms support the following model classes:

• Linear Regression
• Logistic Regression
• Paired Regression Classifier from https://github.com/algowatchpenn/GerryFair
• Perceptron
• Multi-Layer Perceptron for Classification - Uses custom wrapper class to work with our algorithm