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======
The Bias and Fairness Audit Toolkit for Machine Learning
======

.. image:: _static/aequitas_header.png


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 <http://dsapp.uchicago.edu/aequitas/>`_.

Demo
====

`See what Aequitas can do <http://aequitas.dssg.io/>`_.

Sample Jupyter Notebook
====

 `Explore bias analysis of the COMPAS data <https://github.com/dssg/aequitas/blob/master/docs/source/examples/compas_demo.ipynb>`_ using the Aequitas library.

.. toctree::
   :maxdepth: 3
   :glob:
   
   30_seconds_aequitas.ipynb
   installation.ipynb
   input_data.ipynb
   output_data.ipynb
   metrics.ipynb
   CLI.ipynb
   config.ipynb
   using_python.ipynb
   30_seconds_webapp.ipynb
   api/aequitas
   examples/index