aequitas 0.32.0

The bias and fairness audit toolkit.

Copyright (c) 2018. The University of Chicago (''Chicago''). All Rights Reserved.

Permission to use, copy, modify, and distribute this software, including all object code and source code, and any accompanying documentation (together the ''Program'') for educational and not-for-profit research purposes, without fee and without a signed licensing agreement, is hereby granted, provided that the above copyright notice, this paragraph and the following three paragraphs appear in all copies, modifications, and distributions. For the avoidance of doubt, educational and not-for-profit research purposes excludes any service or part of selling a service that uses the Program. To obtain a commercial license for the Program, contact the Technology Commercialization and Licensing, Polsky Center for Entrepreneurship and Innovation, University of Chicago, 1452 East 53rd Street, 2nd floor, Chicago, IL 60615.

Created by Data Science and Public Policy, University of Chicago

The Program is copyrighted by Chicago. The Program is supplied ''as is'', without any accompanying services from Chicago. Chicago does not warrant that the operation of the Program will be uninterrupted or error-free. The end-user understands that the Program was developed for research purposes and is advised not to rely exclusively on the Program for any reason.

IN NO EVENT SHALL CHICAGO BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THE PROGRAM, EVEN IF CHICAGO HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. CHICAGO SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE PROGRAM PROVIDED HEREUNDER IS PROVIDED "AS IS". CHICAGO HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

Description:
Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive tools.
Keywords: fairness bias aequitas
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown