lacava / fairness-in-ml

This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.

Fairness in Machine Learning

This project demonstrates how 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!