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image:: https://img.shields.io/badge/docs-passing-brightgreen.svg\n    :target: https://docs.responsibly.ai\n\n.. image:: https://img.shields.io/gitter/room/nwjs/nw.js.svg\n   :alt: Join the chat at https://gitter.im/ResponsiblyAI/responsibly\n   :target: https://gitter.im/ResponsiblyAI/responsibly\n\n.. image:: https://img.shields.io/github/workflow/status/ResponsiblyAI/responsibly/CI/master.svg\n    :target: https://github.com/ResponsiblyAI/responsibly/actions/workflows/ci.yml\n \n.. image::  https://img.shields.io/coveralls/ResponsiblyAI/responsibly/master.svg\n   :target: https://coveralls.io/r/ResponsiblyAI/responsibly\n\n.. image::  https://img.shields.io/scrutinizer/g/ResponsiblyAI/responsibly.svg\n  :target: https://scrutinizer-ci.com/g/ResponsiblyAI/responsibly/?branch=master\n\n.. image::  https://img.shields.io/pypi/v/responsibly.svg\n  :target: https://pypi.org/project/responsibly\n\n.. image::  https://img.shields.io/github/license/ResponsiblyAI/responsibly.svg\n    :target: https://docs.responsibly.ai/about/license.html\n\n**Toolkit for Auditing and Mitigating Bias and Fairness**\n**of Machine Learning Systems 🔎🤖🧰**\n\n*Responsibly* is developed for **practitioners** and **researchers** in mind,\nbut also for learners. Therefore, it is compatible with\ndata science and machine learning tools of trade in Python,\nsuch as Numpy, Pandas, and especially **scikit-learn**.\n\nThe primary goal is to be one-shop-stop for **auditing** bias\nand fairness of machine learning systems, and the secondary one\nis to mitigate bias and adjust fairness through\n**algorithmic interventions**.\nBesides, there is a particular focus on **NLP** models.\n\n*Responsibly* consists of three sub-packages:\n\n1. ``responsibly.dataset``\n     Collection of common benchmark datasets from fairness research.\n\n2. ``responsibly.fairness``\n     Demographic fairness in binary classification,\n     including metrics and algorithmic interventions.\n\n3. ``responsibly.we``\n     Metrics and debiasing methods for bias (such as gender and race)\n     in word embedding.\n\nFor fairness, *Responsibly*'s functionality is aligned with the book\n`Fairness and Machine Learning\n- Limitations and Opportunities \u003chttps://fairmlbook.org\u003e`_\nby Solon Barocas, Moritz Hardt and Arvind Narayanan.\n\nIf you would like to ask for a feature or report a bug,\nplease open a\n`new issue \u003chttps://github.com/ResponsiblyAI/responsibly/issues/new\u003e`_\nor write us in `Gitter \u003chttps://gitter.im/ResponsiblyAI/responsibly\u003e`_.\n\nRequirements\n------------\n\n-  Python 3.6+\n\nInstallation\n------------\n\nInstall responsibly with pip:\n\n.. code:: sh\n\n   $ pip install responsibly\n\nor directly from the source code:\n\n.. code:: sh\n\n   $ git clone https://github.com/ResponsiblyAI/responsibly.git\n   $ cd responsibly\n   $ python setup.py install\n\nCitation\n--------\n\nIf you have used *Responsibly* in a scientific publication,\nwe would appreciate citations to the following:\n\n::\n\n  @Misc{,\n    author = {Shlomi Hod},\n    title =  {{Responsibly}: Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems},\n    year =   {2018--},\n    url =    \"http://docs.responsibly.ai/\",\n    note =   {[Online; accessed \u003ctoday\u003e]}\n  }\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fresponsiblyai%2Fresponsibly","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fresponsiblyai%2Fresponsibly","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fresponsiblyai%2Fresponsibly/lists"}