fairness-in-ml
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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!
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