Fairlearn Get Started User Guide API Docs Example Notebooks Contributor Guide FAQ About Us Improve fairness of AI systems Fairlearn is an open-source, community-driven project to help data scientists improve fairness of AI systems. Learn about AI fairness from our guides and use cases. Assess and mitigate fairness issues using our Python toolkit. Join our community and contribute metrics, algorithms, and other resources. Get Started FAIRNESS IS SOCIOTECHNICAL Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library. Throughout this website, you can find resources on how to think about fairness as sociotechnical, and how to use Fairlearn's metrics and algorithms while considering the AI system's broader societal context. USE CASE | CREDIT-CARD LOANS Assessment and mitigation of fairness issues in credit-card default models When making a decision to approve or decline a loan, financial services organizations use a variety of models, including a model that predicts the applicant's probability of default. These predictions are sometimes used to automatically reject or accept an application, directly impacting both the applicant and the organization. In this scenario, fairness-related harms may arise when the model makes more mistakes for some groups of applicants compared to others. We use Fairlearn to assess how different groups, defined in terms of their sex, are affected and how the observed disparities may be mitigated. Jupyter Notebook Get started with Fairlearn To get started, install the Fairlearn package. But the process does not end there! See our user guide and other resources to understand what fairness means for your use case. If you run into any issues, reach out on Discord. Fairlearn can be installed with pip from PyPI as follows pip install fairlearn Quickstart Guide Resources User Guide Learn more about fairness in AI, fairness metrics, and mitigation algorithms. API Documentation Library reference with examples. Contributor Guide Help us with case studies, documentation, or code. There are many ways to contribute, regardless of your background or expertise. Join the community Fairlearn community consists of open source contributors, data science practitioners, and responsible AI enthusiasts across many disciplines and locations. Join the latest conversation, ask usage questions, learn about the most recent updates, and find out how to get involved. Discord Join the community and ask usage questions. StackOverflow Ask usage questions. GitHub Contribute code, documentation, use cases. Submit bug reports and feature requests. Twitter Follow us for the latest updates. Contribute to Fairlearn Fairlearn is built and maintained by open source contributors with a variety of backgrounds and expertise. Join the effort and contribute feedback, metrics, algorithms, visualizations, ideas and more, so we can evolve the toolkit together! Contributor Guide #fairlearn Copyright © Fairlearn contributors.