Package: fairlearn
|MIT license| |PyPI| |Discord| |StackOverflow|FairlearnFairlearn 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/Current release <#current-release>__What we mean by *fairness* <#what-we-mean-by-fairness>__Overview of Fairlearn <#overview-of-fairlearn>__Fairlearn metrics <#fairlearn-metrics>__Fairlearn algorithms <#fairlearn-algorithms>__Install Fairlearn <#install-fairlearn>__Usage <#usage>__Contributing <#contributing>__Maintainers <#maintainers>__Issues <#issues>__Current releaseThe current stable release is available onPyPI <https://pypi.org/project/fairlearn/>__.Our current version may differ substantially from earlier versions.
Users of earlier versions should visit ourversion guide <https://fairlearn.org/main/user_guide/installation_and_version_guide/version_guide.html>_
to navigate significant changes and find information on how to migrate.What we mean byfairnessAn 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 asgroup 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 ouruser guide on Fairness of AI Systems <https://fairlearn.org/main/user_guide/fairness_in_machine_learning.html#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 FairlearnThe Fairlearn Python package has two components:Metricsfor assessing which groups are negatively impacted by a
model, and for comparing multiple models in terms of various fairness
and accuracy metrics.Algorithmsfor mitigating unfairness in a variety of AI tasks and
along a variety of fairness definitions.Fairlearn metricsCheck out our in-depth `guide on the Fairlearn
metrics <https://fairlearn.org/main/user_guide/assessment>`__.

Fairlearn algorithmsFor an overview of our algorithms please refer to ourwebsite <https://fairlearn.org/main/user_guide/mitigation/index.html>__.Install FairlearnFor instructions on how to install Fairlearn check out ourQuickstart guide <https://fairlearn.org/main/quickstart.html>__.UsageFor common usage refer to theJupyter notebooks <https://fairlearn.org/main/auto_examples/index.html>__ and
ouruser guide <https://fairlearn.org/main/user_guide/index.html>.
Please note that our APIs are subject to change, so notebooks downloaded
frommainmay not be compatible with Fairlearn installed withpip. In this case, please navigate the tags in the repository (e.g.v0.7.0 <https://github.com/fairlearn/fairlearn/tree/v0.7.0>) to
locate the appropriate version of the notebook.ContributingTo contribute please check ourcontributor guide <https://fairlearn.org/main/contributor_guide/index.html>__.MaintainersA list of current maintainers ison our website <https://fairlearn.org/main/about/index.html>__.IssuesUsage QuestionsPose questions and help answer them on `Stack
Overflow <https://stackoverflow.com/questions/tagged/fairlearn>`__ with
the tag ``fairlearn`` or on
`Discord <https://discord.gg/R22yCfgsRn>`__.

Regular (non-security) issuesIssues are meant for bugs, feature requests, and documentation
improvements. Please submit a report throughGitHub issues <https://github.com/fairlearn/fairlearn/issues>__.
A maintainer will respond promptly as appropriate.Maintainers will try to link duplicate issues when possible.Reporting security issuesTo 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