Skip to content
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Branch: master
Clone or download
Latest commit 7fbe0cd Jan 25, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
aif360 Merge pull request #65 from IBM/style_fixes Jan 20, 2019
docs Add files via upload Jan 25, 2019
examples Merge pull request #62 from IBM/credit_tutor_reweigh Jan 20, 2019
tests added random seed for meta classifier test script Oct 16, 2018
.gitignore
.travis.yml double checking that numpy 1.15 is installed Jan 15, 2019
CONTRIBUTING.md
LICENSE Initial revision Sep 11, 2018
MAINTAINERS.md Update MAINTAINERS.md Sep 17, 2018
MANIFEST.in Initial revision Sep 11, 2018
README.md
requirements.txt restrict numpy version below 1.16 Jan 15, 2019
setup.py version bump 0.1.1 -> 0.2.0 Jan 23, 2019

README.md

AI Fairness 360 (AIF360 v0.2.0)

Build Status Documentation PyPI version

The AI Fairness 360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.

Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. To help, we have created some guidance material that can be consulted.

We have developed the package with extensibility in mind. This library is still in development. We encourage the contribution of your metrics, explainers, and debiasing algorithms.

Get in touch with us on Slack (invitation here)!

Supported bias mitigation algorithms

Supported fairness metrics

  • Comprehensive set of group fairness metrics derived from selection rates and error rates
  • Comprehensive set of sample distortion metrics
  • Generalized Entropy Index (Speicher et al., 2018)

Setup

Supported Configurations:

OS Python version
macOS 2.7, 3.5, 3.6
Ubuntu 2.7, 3.5, 3.6
Windows 3.5

Installation is easiest on a Unix-like system running Python 3. See the Troubleshooting section if you have issues with other configurations.

(Optional) Create a virtual environment

AIF360 requires specific versions of many Python packages which may conflict with other projects on your system. A virtual environment manager is strongly recommended to ensure dependencies may be installed safely. If you have trouble installing AIF360, try this first.

Conda

Conda is recommended for all configurations though Virtualenv is generally interchangeable for our purposes (CVXPY may require conda in some cases). Miniconda is sufficient (see the difference between Anaconda and Miniconda if you are curious) and can be installed from here if you do not already have it.

Then, to create a new Python 3.5 environment, run:

conda create --name aif360 python=3.5
conda activate aif360

The shell should now look like (aif360) $. To deactivate the environment, run:

(aif360)$ conda deactivate

The prompt will return to $ .

Note: Older versions of conda may use source activate aif360 and source deactivate (activate aif360 and deactivate on Windows).

Install with minimal dependencies

To install the latest stable version from PyPI, run:

pip install aif360

This package supports Python 2.7, 3.5, and 3.6. However, for Python 2, the BlackBoxAuditing package must be installed manually.

Some algorithms require additional dependencies not included in the minimal installation. To use these, we recommend a full installation.

Full installation

Clone the latest version of this repository:

git clone https://github.com/IBM/AIF360

If you'd like to run the examples, download the datasets now and place them in their respective folders as described in aif360/data/README.md.

Then, navigate to the root directory of the project and run:

pip install .

Run the Examples

To run the example notebooks, install the additional requirements as follows:

pip install -r requirements.txt

Then, follow the Getting Started instructions from PyTorch to download and install the latest version for your machine.

Finally, if you did not already, download the datasets as described in aif360/data/README.md but place them in the appropriate sub-folder in $ANACONDA_PATH/envs/aif360/lib/python3.5/site-packages/aif360/data/raw where $ANACONDA_PATH is the base path to your conda installation (e.g. ~/anaconda).

Troubleshooting

If you encounter any errors during the installation process, look for your issue here and try the solutions.

TensorFlow

In some cases, the URL is required for installation:

# WINDOWS
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.1.0-cp35-cp35m-win_amd64.whl

# MACOS
pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.1.0-py3-none-any.whl

# LINUX
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.1.0-cp36-cp36m-linux_x86_64.whl

Substitute Python version numbers for your configuration as appropriate (Note: TensorFlow 1.1.0 only supports Python 3.5 officially on Windows).

TensorFlow is only required for use with the aif360.algorithms.inprocessing.AdversarialDebiasing class.

CVXPY

On Windows, you may need to download the appropriate Visual Studio C++ compiler for Python. Then, re-run:

pip install -r requirements.txt

See the CVXPY Installation Instructions for an alternate installation procedure using conda.

CVXPY is only required for use with the aif360.algorithms.preprocessing.OptimPreproc class.

BlackBoxAuditing

Some additional installation is required to use aif360.algorithms.preprocessing.DisparateImpactRemover with Python 2.7. In a directory of your choosing, run:

git clone https://github.com/algofairness/BlackBoxAuditing

In the root directory of BlackBoxAuditing, run:

echo -n $PWD/BlackBoxAuditing/weka.jar > python2_source/BlackBoxAuditing/model_factories/weka.path
echo "include python2_source/BlackBoxAuditing/model_factories/weka.path" >> MANIFEST.in
pip install --no-deps .

This will produce a minimal installation which satisfies our requirements.

Using AIF360

The examples directory contains a diverse collection of jupyter notebooks that use AI Fairness 360 in various ways. Both tutorials and demos illustrate working code using AIF360. Tutorials provide additional discussion that walks the user through the various steps of the notebook. See the details about tutorials and demos here

Citing AIF360

A technical description of AI Fairness 360 is available in this paper. Below is the bibtex entry for this paper.

@misc{aif360-oct-2018,
    title = "{AI Fairness} 360:  An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias",
    author = {Rachel K. E. Bellamy and Kuntal Dey and Michael Hind and
	Samuel C. Hoffman and Stephanie Houde and Kalapriya Kannan and
	Pranay Lohia and Jacquelyn Martino and Sameep Mehta and
	Aleksandra Mojsilovic and Seema Nagar and Karthikeyan Natesan Ramamurthy and
	John Richards and Diptikalyan Saha and Prasanna Sattigeri and
	Moninder Singh and Kush R. Varshney and Yunfeng Zhang},
    month = oct,
    year = {2018},
    url = {https://arxiv.org/abs/1810.01943}
}

AIF360 Videos

  • Introductory video to AI Fairness 360 by Kush Varshney, September 20, 2018 (32 mins)
You can’t perform that action at this time.