Metadata-Version: 2.1
Name: fairlearn
Version: 0.6.1
Summary: Algorithms for mitigating unfairness in supervised machine learning
Home-page: https://github.com/fairlearn/fairlearn
Author: Miroslav Dudik, Richard Edgar, Brandon Horn, Roman Lutz
Author-email: fairlearn@microsoft.com
License: UNKNOWN
Description: [![Build Status](https://dev.azure.com/responsibleai/fairlearn/_apis/build/status/Nightly?branchName=main)](https://dev.azure.com/responsibleai/fairlearn/_build/latest?definitionId=23&branchName=main) ![MIT license](https://img.shields.io/badge/License-MIT-blue.svg) ![PyPI](https://img.shields.io/pypi/v/fairlearn?color=blue) [![Gitter](https://badges.gitter.im/fairlearn/community.svg)](https://gitter.im/fairlearn/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![StackOverflow](https://img.shields.io/badge/StackOverflow-questions-blueviolet)](https://stackoverflow.com/questions/tagged/fairlearn)
        
        # 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 a Jupyter widget for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage.
        
        Website: https://fairlearn.github.io/
        
        - [Current release](https://github.com/fairlearn/fairlearn/tree/v0.6.1#current-release)
        - [What we mean by _fairness_](https://github.com/fairlearn/fairlearn/tree/v0.6.1#what-we-mean-by-fairness)
        - [Overview of Fairlearn](https://github.com/fairlearn/fairlearn/tree/v0.6.1#overview-of-fairlearn)
          - [Fairlearn algorithms](https://github.com/fairlearn/fairlearn/tree/v0.6.1#fairlearn-algorithms)
          - [Fairlearn dashboard](https://github.com/fairlearn/fairlearn/tree/v0.6.1#fairlearn-dashboard)
        - [Install Fairlearn](https://github.com/fairlearn/fairlearn/tree/v0.6.1#install-fairlearn)
        - [Usage](https://github.com/fairlearn/fairlearn/tree/v0.6.1#usage)
        - [Contributing](https://github.com/fairlearn/fairlearn/tree/v0.6.1#contributing)
        - [Maintainers](https://github.com/fairlearn/fairlearn/tree/v0.6.1#maintainers)
        - [Issues](https://github.com/fairlearn/fairlearn/tree/v0.6.1#issues)
        
        ## Current release
        
        - The current stable release is available at [Fairlearn v0.6.1](https://github.com/fairlearn/fairlearn/tree/release/v0.6.1).
        
        - Our current version differs substantially from version 0.2 or earlier. Users of these older versions should visit our [onboarding guide](https://fairlearn.github.io/main/contributor_guide/development_process.html#onboarding-guide).
        
        ## 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 &ndash; 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.github.io/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 Fairlearn
        
        The Fairlearn Python package has two components:
        
        - A _dashboard_ 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 algorithms
        
        For an overview of our algorithms please refer to our [website](https://fairlearn.github.io/main/user_guide/mitigation.html).
        
        ### Fairlearn dashboard
        
        Check out our in-depth [guide on the Fairlearn dashboard](https://fairlearn.github.io/main/user_guide/assessment.html#fairlearn-dashboard).
        
        ## Install Fairlearn
        
        For instructions on how to install Fairlearn check out our [Quickstart guide](https://fairlearn.github.io/main/quickstart.html).
        
        ## Usage
        
        For common usage refer to the [Jupyter notebooks](./notebooks) and our
        [user guide](https://fairlearn.github.io/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. [v0.4.5](https://github.com/fairlearn/fairlearn/tree/v0.4.5))
        to locate the appropriate version of the notebook.
        
        ## Contributing
        
        To contribute please check our
        [contributor guide](https://fairlearn.github.io/main/contributor_guide/index.html).
        
        ## Maintainers
        
        The Fairlearn project is maintained by:
        
        - **@adrinjalali**
        - **@hildeweerts**
        - **@MiroDudik**
        - **@mmadaio**
        - **@riedgar-ms**
        - **@romanlutz**
        
        For a full list of contributors refer to the [authors page](https://github.com/fairlearn/fairlearn/tree/v0.6.1/AUTHORS.md)
        
        ## Issues
        
        ### Usage Questions
        
        Pose questions and help answer them on [Stack
        Overflow](https://stackoverflow.com/questions/tagged/fairlearn) with the tag
        `fairlearn` or on [Gitter](https://gitter.im/fairlearn/community#).
        
        ### 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
        
        Please take a look at our guidelines for reporting [security issues](https://github.com/fairlearn/fairlearn/tree/v0.6.1/SECURITY.md).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: customplots
