ghostintheshellarise/aequitas

Bias and Fairness Audit Toolkit

The Bias and Fairness Audit Toolkit for Machine Learning
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Aequitas
Aequitas is an open-source bias audit toolkit for machine learning developers, analysts, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive risk-assessment tools.
Learn more about the project.
Demo

See what Aequitas can do.

Sample Jupyter Notebook ====

Explore bias analysis of the COMPAS data using the Aequitas library.

Documentation ====

Find documentation here.

Installation

Aequitas requires Python 3.

Install this Python library from source:

python3 setup.py install

...or named as an installation requirement, e.g. via pip:

pip3 install git+https://github.com/dssg/aequitas.git

You may then import the aequitas module from Python:

import aequitas

...or execute the auditor from the command line:

aequitas-report

Development

Provision your development environment via develop:

./develop

Common development tasks, such as deploying the webapp, may then be handled via manage:

manage --help

Find more at the documentation.

To contact the team, please email us at [aequitas at uchicago dot edu]

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© 2018 Center for Data Science and Public Policy - University of Chicago