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ApproxBias documentation
========================

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Welcome to the ApproxBias's documentation. This is to help you reproduce our work from

#. Does machine bring in extra bias in learning? Approximating fairness in models promptly `[arXiv 2405.09251] <https://arxiv.org/abs/2405.09251>`_
#. Approximating discrimination within models when faced with several non-binary sensitive attributes `[arXiv 2408.06099] <https://arxiv.org/abs/2408.06099>`_

We proposed a fairness measure named *harmonic fairness measure via manifolds (HFM)* with three optional versions, which deals with a fine-grained discrimination evaluation for one or more sensitive attributes (sen-att-s). *HFM* relies on the Euclidean Hausdorff distance, of which the direct computation is rather heavy. To accelerate the distance computation, we further proposed a few approximation algorithms for efficient bias evaluation.

- To get started quickly, see :doc:`an example <posts/quickstart>` here
- To understand the methodology, see :doc:`methodology <posts/methodology>`
- To reproduce the empirical results, see :doc:`instructions <posts/reproduction>`
.. - To learn the model evaluation we use, see :doc:`evaluation <posts/evaluation>`


.. - To reproduce our results, see :doc:`reproduction <posts/reproduction>`
.. - To dive into more details, see :doc:`documentation <posts/documentation>`

.. :doc:`methodology.rst <static/methodology>`
.. Welcome to the ApproxBias's documentation. This is to help you reproduce our work in


--------

.. Taking a short cut
.. Shortcuts
.. ==================

SHORTCUTS
^^^^^^^^^^
.. toctree::
   :maxdepth: 1

   posts/quickstart.rst
   posts/methodology.rst
   posts/reproduction.rst

..    posts/evaluation.rst

..    posts/documentation.rst
.. static/methodology.rst



.. .. toctree: :
..    :hidden:
..    :caption: ApproxBias
..    :caption: Reproduction
..
..    posts/reproduction.rst
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