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https://github.com/fairlearn/fairlearn

A Python package to assess and improve fairness of machine learning models.
https://github.com/fairlearn/fairlearn

ai ai-systems artificial-intelligence fairness fairness-ai fairness-assessment fairness-ml group-fairness harms machine-learning responsible-ai unfairness-mitigation

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A Python package to assess and improve fairness of machine learning models.

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README 

        
|MIT license| |PyPI| |Discord| |StackOverflow|



Fairlearn

=========



Fairlearn is a Python package that empowers developers of artificial

intelligence (AI) systems to assess their system's fairness and mitigate

any observed unfairness issues. Fairlearn contains mitigation algorithms

as well as metrics for model assessment. Besides the source code, this

repository also contains Jupyter notebooks with examples of Fairlearn

usage.



Website: https://fairlearn.org/



-  `What we mean by *fairness* <#what-we-mean-by-fairness>`__

-  `Overview of Fairlearn <#overview-of-fairlearn>`__

-  `Getting started with fairlearn <#getting-started-with-fairlearn>`__

-  `Maintainers <#maintainers>`__

-  `Code of conduct <#code-of-conduct>`__

-  `Issues <#issues>`__



What we mean by *fairness*

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



An AI system can behave unfairly for a variety of reasons. In Fairlearn,

we define whether an AI system is behaving unfairly in terms of its

impact on people – i.e., in terms of harms. We focus on two kinds of

harms:



-  *Allocation harms.* These harms can occur when AI systems extend or

   withhold opportunities, resources, or information. Some of the key

   applications are in hiring, school admissions, and lending.



-  *Quality-of-service harms.* Quality of service refers to whether a

   system works as well for one person as it does for another, even if

   no opportunities, resources, or information are extended or withheld.



We follow the approach known as **group fairness**, which asks: *Which

groups of individuals are at risk for experiencing harms?* The relevant

groups need to be specified by the data scientist and are application

specific.



Group fairness is formalized by a set of constraints, which require that

some aspect (or aspects) of the AI system's behavior be comparable

across the groups. The Fairlearn package enables assessment and

mitigation of unfairness under several common definitions. To learn more

about our definitions of fairness, please visit our

`user guide on Fairness of AI Systems `__.



    *Note*: Fairness is fundamentally a sociotechnical challenge. Many

    aspects of fairness, such as justice and due process, are not

    captured by quantitative fairness metrics. Furthermore, there are

    many quantitative fairness metrics which cannot all be satisfied

    simultaneously. Our goal is to enable humans to assess different

    mitigation strategies and then make trade-offs appropriate to their

    scenario.



Overview of Fairlearn

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



The Fairlearn Python package has two components:



-  *Metrics* for assessing which groups are negatively impacted by a

   model, and for comparing multiple models in terms of various fairness

   and accuracy metrics.



-  *Algorithms* for mitigating unfairness in a variety of AI tasks and

   along a variety of fairness definitions.



Fairlearn metrics

~~~~~~~~~~~~~~~~



Check out our in-depth `guide on the Fairlearn metrics `__.



Fairlearn algorithms

~~~~~~~~~~~~~~~~~~~



For an overview of our algorithms please refer to our

`website `__.



Getting Started with Fairlearn

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



First steps

~~~~~~~~~~~



- Install via pip: ``pip install fairlearn``



- Visit the `Quickstart guide `__.



- **Learning Resources**:



  - Read the comprehensive `user guide `__.



  - Look through the `example notebooks `__.



For Users & Practitioners

~~~~~~~~~~~~~~~~~~~~~~~~



- Browse the `example gallery `__. 

Please note that notebooks downloaded from `main` may not be compatible with pip-installed versions.



- Check the `API reference `__.



- **Get Help**:



  - Ask questions on `Stack Overflow `__ with tag ``fairlearn``.



  - Join the `Discord community `__ for discussions.



For Contributors

~~~~~~~~~~~~~~~



- Read the `contributor guide `__.



- Check out the `good first issues `__.



- Follow the `development process `__.



- Join the `Discord `__ for contributor discussions. Please use the ``#development`` channel.



Maintainers

-----------



A list of current maintainers is

`on our website `__.



Code of conduct

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

Fairlearn follows the `Fairlearn Organization's Code of Conduct `__.



Issues

------



Regular (non-security) issues

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Issues are meant for bugs, feature requests, and documentation

improvements. Please submit a report through

`GitHub issues `__.

A maintainer will respond promptly as appropriate.



Maintainers will try to link duplicate issues when possible.



Reporting security issues

~~~~~~~~~~~~~~~~~~~~~~~~~



To report security issues please send an email to

``fairlearn-internal@python.org``.



.. |MIT license| image:: https://img.shields.io/badge/License-MIT-blue.svg

   :target: https://github.com/fairlearn/fairlearn/blob/main/LICENSE

.. |PyPI| image:: https://img.shields.io/pypi/v/fairlearn?color=blue

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

.. |Discord| image:: https://img.shields.io/discord/840099830160031744

   :target: https://discord.gg/R22yCfgsRn

.. |StackOverflow| image:: https://img.shields.io/badge/StackOverflow-questions-blueviolet

   :target: https://stackoverflow.com/questions/tagged/fairlearn