Metadata-Version: 2.1
Name: fare
Version: 0.1
Summary: Fare auditing diagnostics and pairwise error metrics for fair ranking.
Home-page: https://github.com/caitlinkuhlman/fare
Maintainer: Caitlin Kuhlman
Maintainer-email: cakuhlman@wpi.edu
License: new BSD
Download-URL: https://github.com/caitlinkuhlman/fare
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib

This repository contains code and example analysis for evaluating the fairness of rankings with respect to protected groups, using the pairwise error metrics and auditing methodology presented in the paper:

"FARE: Diagnostics for Fair Ranking using Pairwise Error Metrics" in the proceedings of the Web Conference (WWW 2019)
by Caitlin Kuhlman, MaryAnn VanValkenburg, Elke Rundensteiner 

This work is released under the 3-Clause BSD License.

The three pairwise error metrics presented in the paper, *Rank Equality, Rank Parity, and Rank Calibration* are included in the fare package distibution, along with methods to perform fairness auditing of rankings.

Example analysis, including the experiments in the paper, is available in the jupyter notebooks in the examples folder. 


