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 StartedFairness 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.
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.
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.
Learn more about fairness in AI, fairness metrics, and mitigation algorithms.
Library reference with examples.
Help us with case studies, documentation, or code. There are many ways to contribute, regardless of your background or expertise.
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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!
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