https://github.com/ResponsiblyAI/responsibly

Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰
https://github.com/ResponsiblyAI/responsibly

artificial-intelligence audit bias bias-correction bias-finder bias-reduction data-science ethics fairness fairness-ai fairness-awareness-model fairness-ml fairness-testing machine-bias machine-learning natural-language-processing python

Last synced: 2 months ago
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Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰

• Host: GitHub
• URL: https://github.com/ResponsiblyAI/responsibly
• Owner: ResponsiblyAI
• License: mit
• Created: 2018-08-02T11:31:18.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-11-17T17:33:35.000Z (6 months ago)
• Last Synced: 2024-02-27T07:44:02.299Z (3 months ago)
• Topics: artificial-intelligence, audit, bias, bias-correction, bias-finder, bias-reduction, data-science, ethics, fairness, fairness-ai, fairness-awareness-model, fairness-ml, fairness-testing, machine-bias, machine-learning, natural-language-processing, python
• Language: Python
• Homepage: http://docs.responsibly.ai
• Size: 33 MB
• Stars: 87
• Watchers: 6
• Forks: 20
• Open Issues: 12
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

Lists

• awesome-production-machine-learning - responsibly - Toolkit for auditing and mitigating bias and fairness of machine learning systems (Explaining Black Box Models and Datasets)
• awesome-machine-learning-interpretability - responsibly
• awesome-production-machine-learning - responsibly - Toolkit for auditing and mitigating bias and fairness of machine learning systems (Explaining Black Box Models and Datasets)
• Awesome-AIML-Data-Ops - responsibly - Toolkit for auditing and mitigating bias and fairness of machine learning systems (Explaining Black Box Models and Datasets)
• awesome-production-machine-learning - responsibly - Toolkit for auditing and mitigating bias and fairness of machine learning systems (Explaining Black Box Models and Datasets)

README

        
Responsibly

===========

.. image:: https://img.shields.io/badge/docs-passing-brightgreen.svg

    :target: https://docs.responsibly.ai

.. image:: https://img.shields.io/gitter/room/nwjs/nw.js.svg

   :alt: Join the chat at https://gitter.im/ResponsiblyAI/responsibly

   :target: https://gitter.im/ResponsiblyAI/responsibly

.. image:: https://img.shields.io/github/workflow/status/ResponsiblyAI/responsibly/CI/master.svg

    :target: https://github.com/ResponsiblyAI/responsibly/actions/workflows/ci.yml

 

.. image::  https://img.shields.io/coveralls/ResponsiblyAI/responsibly/master.svg

   :target: https://coveralls.io/r/ResponsiblyAI/responsibly

.. image::  https://img.shields.io/scrutinizer/g/ResponsiblyAI/responsibly.svg

  :target: https://scrutinizer-ci.com/g/ResponsiblyAI/responsibly/?branch=master

.. image::  https://img.shields.io/pypi/v/responsibly.svg

  :target: https://pypi.org/project/responsibly

.. image::  https://img.shields.io/github/license/ResponsiblyAI/responsibly.svg

    :target: https://docs.responsibly.ai/about/license.html

**Toolkit for Auditing and Mitigating Bias and Fairness**

**of Machine Learning Systems 🔎🤖🧰**

*Responsibly* is developed for **practitioners** and **researchers** in mind,

but also for learners. Therefore, it is compatible with

data science and machine learning tools of trade in Python,

such as Numpy, Pandas, and especially **scikit-learn**.

The primary goal is to be one-shop-stop for **auditing** bias

and fairness of machine learning systems, and the secondary one

is to mitigate bias and adjust fairness through

**algorithmic interventions**.

Besides, there is a particular focus on **NLP** models.

*Responsibly* consists of three sub-packages:

1. ``responsibly.dataset``

     Collection of common benchmark datasets from fairness research.

2. ``responsibly.fairness``

     Demographic fairness in binary classification,

     including metrics and algorithmic interventions.

3. ``responsibly.we``

     Metrics and debiasing methods for bias (such as gender and race)

     in word embedding.

For fairness, *Responsibly*'s functionality is aligned with the book

`Fairness and Machine Learning

- Limitations and Opportunities `_

by Solon Barocas, Moritz Hardt and Arvind Narayanan.

If you would like to ask for a feature or report a bug,

please open a

`new issue `_

or write us in `Gitter `_.

Requirements

------------

-  Python 3.6+

Installation

------------

Install responsibly with pip:

.. code:: sh

   $ pip install responsibly

or directly from the source code:

.. code:: sh

   $ git clone https://github.com/ResponsiblyAI/responsibly.git

   $ cd responsibly

   $ python setup.py install

Citation

--------

If you have used *Responsibly* in a scientific publication,

we would appreciate citations to the following:

::

  @Misc{,

    author = {Shlomi Hod},

    title =  {{Responsibly}: Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems},

    year =   {2018--},

    url =    "http://docs.responsibly.ai/",

    note =   {[Online; accessed ]}

  }