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

|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>__

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

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

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

• Algorithms for mitigating unfairness in a variety of AI tasks and

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 __.