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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/
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>__The current stable release is available on
PyPI <https://pypi.org/project/fairlearn/>__.
Our current version may differ substantially from earlier versions.
Users of earlier versions should visit our
version 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.
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 <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.
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 <https://fairlearn.org/main/user_guide/assessment>`__.
Fairlearn algorithms
For an overview of our algorithms please refer to our
website <https://fairlearn.org/main/user_guide/mitigation/index.html>__.
For instructions on how to install Fairlearn check out our Quickstart guide <https://fairlearn.org/main/quickstart.html>__.
For common usage refer to the Jupyter notebooks <https://fairlearn.org/main/auto_examples/index.html>__ and
our user guide <https://fairlearn.org/main/user_guide/index.html>.
Please note that our APIs are subject to change, so notebooks downloaded
from main may not be compatible with Fairlearn installed with
pip. 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.
To contribute please check our contributor guide <https://fairlearn.org/main/contributor_guide/index.html>__.
A list of current maintainers is
on our website <https://fairlearn.org/main/about/index.html>__.
Usage Questions
Pose 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) issues
Issues are meant for bugs, feature requests, and documentation
improvements. Please submit a report through
GitHub 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 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
FAQs
A Python package to assess and improve fairness of machine learning models.
We found that fairlearn demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 5 open source maintainers collaborating on the project.
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