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Welcome to fairret's documentation!
===================================

.. figure:: _static/Overview_fairret.png
   :width: 680px
   :alt: Overview of fairret structure
   :align: left

The goal of fairret is to serve as an open-source Python library for measuring and mitigating statistical fairness in PyTorch models. The library is designed to be 

1. *flexible* in how fairness is defined and pursued.
2. *easy* to integrate into existing PyTorch pipelines.
3. *clear* in what its tools can and cannot do.

The central to the library is the paradigm of the *fairness regularization term* (fairrets) that quantify unfairness as differentiable PyTorch loss functions. 
These can then be optimized together with e.g. the binary cross-entropy error such that the classifier improves both its accuracy and fairness.

.. note::

   This project is under active development!

Explore:

.. toctree::
   :maxdepth: 1

   loss
   statistic
   metric
   examples



Citation
--------

If you have found fairret useful in your research, please cite our ICLR 2024 paper_:

.. _paper: https://openreview.net/pdf?id=NnyD0Rjx2B

.. code-block:: console

   @inproceedings{buyl2024fairret,
   title={fairret: a Framework for Differentiable Fairness Regularization Terms},
   author={Buyl, Maarten and Defrance, Marybeth and De Bie, Tijl},
   booktitle={International Conference on Learning Representations},
   year={2024}}

Indices and tables
----------------------

* :ref:`genindex`
* :ref:`modindex`