Fairness in Machine Learning

This project demonstrates how to make fair machine-learning models.

Notebooks

• fairness-in-ml.ipynb: Keras & TensorFlow implementation of Towards fairness in ML with adversarial networks.
• fairness-in-torch.ipynb: PyTorch implementation of Fairness in Machine Learning with PyTorch.
• playground/*: Various experiments.

Getting started

This repo uses conda’s virtual environment for Python 3.

Install (mini)conda if not yet installed.

For MacOS:

$ wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh
$ chmod +x miniconda.sh
$ ./miniconda.sh -b

cd into this directory and create the conda virtual environment for Python 3 from environment.yml:

$ conda env create -f environment.yml

Activate the virtual environment:

$ source activate fairness-in-ml

Install the fairness library:

$ python setup.py develop

Contributing

If you have applied these models to a different dataset or implemented any other fair models, consider submitting a Pull Request!