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• ↓ Assessment and mitigation of fairness issues in credit-card default models
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"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."

"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."

"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."

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Assessment and mitigation of fairness issues in credit-card default models

Get started with Fairlearn

Resources

Join the community

Contribute to Fairlearn